Public safety analytics gateway

ABSTRACT

A public safety analytics gateway includes a front end processor configured to communicate with a network gateway and a public safety enterprise server and a data collector in communication with the front end processor, wherein the front end processor is configured to receive public safety data from the public service enterprise server and forward the public safety data to both the network gateway and the data collector.

TECHNICAL FIELD

This disclosure relates generally to mobile network architecture andmanagement for public safety uses and, more specifically, to a systemand method for providing data analytics to public safety firstresponders in a timely and efficient manner.

BACKGROUND

Public safety first responders rely on highly-available,low-latency-access communications and network infrastructures,reflecting the overarching public safety community's requirement torespond to the entire spectrum of routine, emergency, and disasteremergency scenarios—both natural and manmade—at a moment's notice.Rapid, real-time data collection and actionable analytics based on thosedata are equally central to first responder immediate andproperly-directed responses.

Most disasters occur without warning, and all require a rapid andflawless response with no room for error. Even if there is a warning,for example, with an approaching hurricane, the location and severity ofthe hurricane is not necessarily known in advance. Timely,multi-disciplinary, coordinated responses across agency lines aremission-critical to protect the communities and citizens that publicsafety first responders are charged to serve. Whether the event is afire, natural disaster, vehicular collision, act of terrorism, orapprehension of suspects, highly-available, low access-latency networks,real-time data collection, and reliable, actionable analytics providethe common denominator to successful rapid response.

However, most if not all first responders do not have access to timely,actionable data analytics, especially in disaster scenarios that relyupon highly-available mobile communications and collaboration.

There is a need to create a system, architecture and method to providefirst responders timely access to mission critical analytics and thenetworks to support efficient low-latency, high throughput, low responsetime, highly available communications networks based on those analytics.

SUMMARY

The present disclosure is directed to a public safety analytics gateway(PSAG) including a front end processor configured to communicate with anetwork gateway and a public safety enterprise server and a datacollector in communication with the front end processor, wherein thefront end processor is configured to receive public safety data from thepublic service enterprise server and forward the public safety data toboth the network gateway and the data collector. The public safetyanalytics gateway may further include a recommendation engine incommunication with the front end processor, the recommendation engineconfigured to capture mobile search data associated with a firstresponder user device, store the mobile search data, and generatemetadata associated with the mobile search data. The public safetyanalytics gateway may further include a data analytics engine incommunication with the data collector and wherein the data analyticsengine is configured to analyze public safety data wherein the dataanalytics comprises one of descriptive analytics, diagnostic analytics,predictive analytics, and prescriptive analytics.

In an aspect, the data analytics is used to monitor mobile applicationat least one of access latency, throughput, response time, andavailability wherein mobile access applications comprise one of voice,data, video, graphics, and text applications for first responders. Therecommendation engine may be in communication with the front endprocessor wherein the recommendation engine configured to capture mobilesearch data associated with a first responder user device, store themobile search data, and generate metadata associated with the mobilesearch data.

In an aspect, the public safety analytics gateway may include anartificial intelligence engine in communication with the recommendationengine and the data analytics engine wherein the artificial intelligenceengine is configured to store a plurality of records and generateoutcomes from interaction with field-based sensors thereby facilitatingpredictive alert conditions and response scenarios.

The public safety analytics gateway may be in communication withexternal servers and/or with a second public safety analytics gatewayapparatus in a network. The public safety analytics gateway may providea central point of integration of public safety embedded control data onbehalf of a plurality of distributed Radio Access Network (RAN)elements.

The disclosure is also directed to an apparatus including aninput-output interface, a processor coupled to the input-outputinterface and wherein the processor is coupled to a memory, the memoryhaving stored thereon executable instructions that when executed by theprocessor cause the processor to effectuate operations includingreceiving data associated with public safety and data not associatedwith public safety, discerning the data associated with public safetyfrom the data not associated with public safety, capturing the dataassociated with public safety and transmitting the data associated withpublic safety and the data not associated with public safety to agateway. The operations may further include identifying search patternsfrom a first responder user device, applying metadata to the searchpatterns, refining the search patterns based on the identifying step,and generating search recommendations as a function of the metadata andan identification of the first responder user device.

In an aspect, the operations may further include causing the dataassociated with public safety to be stored. The operations may furtherinclude analyzing the data associated with public safety in real timeand adjusting a data collection process based on whether there is anemergency event. The operations may further include tracking a locationof emergency vehicles or a location of first responders. The operationsmay further include receiving data from one of a plurality of serversand the server may be configured to aggregate data associated withpublic safety from a plurality of similarly configured apparatuses. Theoperations may further include integrating data from a plurality ofpublic safety mobile applications and a plurality of first responderuser devices.

The disclosure is also directed to a wireless network including anetwork gateway in communication with a plurality of applicationservers, a public safety analytics gateway in communication with theplurality of gateways, the public safety analytics gateway comprising afront end processor configured to communicate with the network gatewayand a public safety enterprise server and a data collector incommunication with the front end processor wherein the front endprocessor is configured to receive public safety data from the publicservice enterprise server and forward the public safety data to both thenetwork gateway and the data collector. The wireless network may furtherinclude a plurality of network gateways and a plurality of public safetyanalytics gateways wherein each public safety analytics gateway of theplurality of public safety analytics gateways is in communication withone or more network gateways of the plurality of network gateways, and amaster public safety analytics gateway in communication with each of theplurality of public safety analytics gateways, wherein the master publicsafety analytics gateway collects public safety data from the pluralityof public safety analytics gateways.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide an understanding ofthe variations in implementing the disclosed technology. However, theinstant disclosure may take many different forms and should not beconstrued as limited to the examples set forth herein. Where practical,like numbers refer to like elements throughout.

FIG. 1 is a representation of an exemplary network configuration showingPublic Safety Analytics Gateway elements in accordance with the presentdisclosure.

FIG. 2 is a representation of an exemplary network configuration showingPublic Safety Analytics Gateway elements connected to a PSAG MasterGateway in accordance with the present disclosure.

FIG. 3 is an alternative embodiment of the network in FIG. 1 furtherincluding a Public Safety Analytics Gateway mediator element within amulti-carrier network environment.

FIG. 4 is a representation of an exemplary network configuration showingPublic Safety Analytics Gateway elements within a multi-carrier networkconnected to a plurality of Public Safety Entity (PSE) web andapplication servers following direct connection via the FIG. 3 PSAGmediator element.

FIG. 5 is a representation of another exemplary network configurationshowing Public Safety Analytics Gateway elements in accordance with thepresent disclosure.

FIG. 6 is an exemplary functional diagram of a Public Safety AnalyticsGateway.

FIG. 7 is an exemplary functional diagram of a Public Safety AnalyticsGateway showing exemplary application programming interfaces.

FIG. 8 is an exemplary flow diagram illustrating a method for collectionof emergency data.

FIG. 9 is an exemplary graph highlighting big data analyticsfunctionality as applied to the present disclosure.

FIG. 10a is an exemplary flow diagram illustrating a method of analyzingemergency data.

FIG. 10b is a continuation of the exemplary flow diagram illustrating amethod of analyzing emergency data from FIG. 10 a.

FIG. 11 is an exemplary flow diagram continuing the illustration of amethod of analyzing emergency data from FIGS. 10a and 10 b.

FIG. 12 is a graphical representation of an analysis of the throughputand latency impacts of additional network loading on a network.

FIG. 13 is an exemplary flow diagram illustrating a method of predictingand measuring latency and throughput, and maintaining acceptable levelsof latency in a network.

FIG. 14 is a representation of an exemplary software defined network.

FIG. 15 is a representation of an exemplary hardware platform for anetwork.

FIG. 16 is an illustration of a functional block diagram depicting oneexample of a Long Term Evolution-Evolved Packet System (LTE-EPS) networkarchitecture.

FIG. 17 depicts an exemplary diagrammatic representation of a machine inthe form of a computer system.

FIG. 18 illustrates a base station with a direct connection to Internet.

FIG. 19 is an example system including RAN and core network.

FIG. 20 illustrates an overall block diagram of an example packet-basedmobile cellular network environment.

FIG. 21 illustrates an architecture of a typical General Packet RadioService (GPRS) network.

FIG. 22 illustrates a Public Land Mobile Network (PLMN) block diagramview of an example architecture that may be replaced by atelecommunications system.

DETAILED DESCRIPTION

Overview.

As detailed herein, the present disclosure is directed to a system andmethod for providing data analytics to public safety professionals,including first responders. A new network element is disclosedcomprising a Public Service Analytics Gateway (PSAG). In an aspect, thePSAG will reside in a core mobile telecommunications network and may,for example, provide front-end processing for Long Term Evolution (LTE)and third generation (3G) General Packet Radio Service Support Node(GGSN) carrier core network gateways.

In an aspect, the PSAG may include an Artificial Intelligence (AI)element incorporating deep learning components, thereby enabling the AIengine to store a plurality of records and “learn” from interaction withfield-based sensors. The PSAG AI engine may, for example, be bothpredictive and prescriptive in its analytics, thereby facilitatingtimely forward-predictive alert conditions and response scenarios.

In an aspect, the PSAG AI-based predictive and prescriptive analyticselements may proactively and dynamically respond to mobile networkaccess latency and throughput degradation, which if unattended, alsoadversely impact response time and network availability. In this manner,the PSAG may dynamically interface with network operations andOperations Support Systems (OSS) systems in near-real time toproactively modify logical topologies and network resource allocationsin order to continually assure low access latency, high throughput, andsufficient Quality of Service (QoS)/Priority Preemption (QPP) to publicsafety agencies and the first responders.

Furthermore, mobility network access latency and throughput degradationissues would most likely compound during metropolitan, regional, state,or national emergency events, based on extraordinary volumes ofmassively parallel emergency service data, voice, and video trafficaccess requests sufficient to compromise traffic shaping andpolicy-based network infrastructures at the worst possible junctures.The present disclosure in which PSAG-resident predictive andprescriptive analytics is further described may avert on a near-realtime basis such latency and throughput degradation, and attendantresponse time and network availability degradation.

System Architecture.

In an aspect, PSAG 15 is envisioned to reside in a coretelecommunications network. With reference to FIG. 1, there is shown anexemplary system 10 comprising external access points 11 and an InternetProtocol/Multiprotocol Label Switching (IP/MPLS) converged network,which will be referred to herein as network 14. Examples of the externalaccess points shown include a public switched telephone network (PSTN)access point 16, an internet access point 18, a wide-area local areanetwork (WLAN) access point 20 and a wireless access point 22. Suchexternal access points 11 are exemplary only and may include otheraccess points including virtual private network (VPN) access points,Wi-Fi access points, and any other access points capable of interfacingwith the IP/MPLS converged network 14. While an IP/MPLS convergednetwork 14 is shown as an example, the disclosure is applicable to anytype of wireless communication network, including 3G, fourth generation(4G)/LTE, fifth generation (5G), and any other wireless communicationnetwork.

Within the network 14, there is shown a series of gateways 12 which may,for example, be Long Term Evolution (LTE) gateways and/or thirdgeneration (3G) Gateway General Packet Radio Service Support Node (GGSN)carrier core network gateways shown in more detail in FIG. 5. Thesegateways 12 may provide network 14 access to a series of servers as setforth below.

A series of servers forms part of the system 10 and may be accessedthrough gateways 1. These servers, for example, include but are notlimited to an Operations Support System/Business Support System(OSS/BSS) server 24, a collaboration application server 26, voiceapplication server 28, conferencing application server 30, messagingapplication server 32, video application server 34, location-basedapplication server 35, presence application server 36, Emergency911/Next Generation 911 (E911/NG911) application server 38 andpush-to-talk application server 40. It will be understood that theseexemplary servers are referred to generically by function and mayinclude two or more of these exemplary servers may reside on one ormultiple hardware components. It is also understood that one or more ofthese exemplary servers may be comprised of proprietary special-purposehardware or be implemented using virtual machines in a software definednetwork.

Also shown in FIG. 1 is a series of Public Safety Analytics Gateways(PSAGs) 15. In an aspect, PSAG 15 may reside in the core network 14, andmay front-end gateways 12, including LTE gateways and GGSN core networkgateways. In addition, PSAG 15 may be implemented as a front-endprocessor (FEP), or as a software-defined network (SDN) element in ahomogenous software defined network or a hybrid SDN network. PSAG 15 mayalso be implemented as a cloud network component and may reside within acloud computing environment. The PSAG 15 may access public safety mobileapplications through a plurality of internal and external interfaces.Such mobile applications may, for example, include computer-aideddispatch, E911, incident command and control, first responder data, bodycamera data, and the like.

PSAG 15 may provide a data analytics application programming interface(API)-based structured programming interface on behalf of public safetyagencies and other enterprises that provide support and service to suchagencies. PSAG 15 may collect data from one or more multiple publicsafety entities and from one or more mobile applications accessible andused by such entities and/or first responders. Data collected may, forexample, be stored and accessible in a data lakes repository 42. In anaspect, these data in data repositories 42 may reside in a cloud storageenvironment.

Among the functions of the PSAG 15 is the generation of ahighly-available public safety data analytics. The PSAG 15 mayfacilitate having a single data analytics image across a plurality ofpublic safety-centric, mobile computing-based, heterogeneous processingplatforms and runtime environments on behalf of first responders. Suchan image may, for example, be from a user's perspective in anoperational environment at a public safety enterprise (PSE) which mayrender various user interfaces and operating systems transparent to thePSE even across heterogeneous platforms.

In an aspect, PSAG 15 may include an Artificial Intelligence (AI)element incorporating deep learning components, enabling the AI engineto store or access a plurality of records and “learn” from interactionof field-based sensors which may, for example, communicate with PSAG 15or other network elements or external elements through an internet ofthings (IoT) protocol. The PSAG 15 AI engine, described in more detailbelow, would be both predictive and prescriptive, thereby facilitatingtimely forward-predictive alert conditions and response scenarios.

With PSAG 15 placed within the network 14, PSAG 15 may act as a centralpoint of integration within and among a set of public safetyapplications and implementations. For example, in an aspect, PSAG 15 mayprovide a central point of integration within and among a plurality ofpublic safety-centric mobile applications through a single carrier corenetwork LTE gateway, or through a plurality of carrier core networks LTEgateways. In another aspect, PSAG 15 may provide a central point ofintegration within and among a plurality of public safety mobileapplications through a software-defined element as a functional supersetof a carrier core network Commercial Connectivity Service (CCS) LTEgateway, or through a plurality of carrier core network CCS LTEgateways. In another aspect, PSAG 15 may provide a central point ofintegration within and among a plurality of public safety mobileapplications through a carrier core network GGSN gateway, or through aplurality of carrier core network GGSN gateways.

In another aspect, PSAG 15 may provide a central point of integrationwithin and among a plurality of public safety mobile applications andmobile devices, or within and among a plurality of public safety mobileapplications for Internet Protocol (IP) data originating from SignalingSystem 7 (SS7) packet(s). In an aspect, PSAG 15 may provide a centralpoint of integration within and among a plurality of public safetymobile applications and public safety mobile devices for short messageservice (SMS) data and or multimedia messaging service (MMS) data.

In another aspect, PSAG 15 may provide a central point of integrationfor public safety location-based services and Global Positioning System(GPS) coordinate-based data. In another aspect, PSAG 15 may provide acentral point of integration within and among a plurality of publicsafety mobile applications and public safety mobile devices to optimizedynamic Quality of Service (QoS), Priority and Preemption (QPP). In yetanother aspect, PSAG 15 may provide a central point of dynamic,software-defined management, control and provisioning function withinand among a plurality of carrier-based core networks, similar to orcomplementary with network 14.

While FIG. 1 illustrates the PSAG 15 within network 14, in anotherembodiment, there may be a PSAG mediator 45 shown in FIG. 2 that residesoutside of network 14. PSAG mediator 45 residing outside of a carriernetwork may function in a domain placed between the carrier PSAG 15 andone or more public safety entity (PSE) enterprise mobile applications.PSAGs 15 resident in the carrier core network 14 may communicate withPSE mobile applications using one or more application programminginterfaces (APIs). The PSAG mediator 145 may then translatecommunication requests between the mobile application and the PSAG 15requests into subsequent requests to individual carrier PSAG APIs. Thisembodiment would enable PSEs to interface with a single global PSAGmediator 145 rather than to a variety of carrier-specific PSAGs such asPSAG 15. This embodiment may facilitate more efficient communicationsbetween and interoperability among multiple carriers and one or morePSEs.

FIG. 3 is an exemplary embodiment in which the PSAG mediator 145 isshown as the mediator between two carrier networks, carrier 1 network 48and carrier 2 network 49. Within carrier 1 network 48, there is shownPSAG 115 a in communication with PSAG gateway 145 and carrier 1 network50 a. Part of carrier 1 network 50 a shown includes legacy 2G/3G RANnetwork components 51 a which interface with mobile clients 53 a and LTERAN network components 42 a. Within carrier 2 network 49, there is shownPSAG 115 b in communication with PSAG gateway 145 and carrier 2 network50 b. Part of carrier 2 network 50 b shown includes legacy 2G/3G RANnetwork components 51 b which interface with mobile clients 53 b and LTERAN network components 51 b

As shown in FIG. 3, each of the carrier 1 networks 48 and the carrier 2networks 49 may be in communication with one or more PSE web andapplications servers 55 through the PSAG mediator 145 connected to theinternet 54 a. In an alternative embodiment shown in FIG. 4, PSAGmediator 145 is not included and each of the carrier 1 networks 148 andthe carrier 2 networks 149 is shown in direct communication with one ormore PSE web and applications servers 55 through the internet 54 b.

In an aspect, a PSAG appliance (not shown) may reside in a PSE networkand would interface closely with a carrier network-resident PSAG 115 a.The PSAG appliance may receive integrated and correlated records, andcommunicate with PSAG 115 a in a secure manner in order to requestfurther information or to receive data analytics.

FIG. 5 illustrates a more detailed view of an exemplary architecture ofFIGS. 3 and 4 showing a mobile network single carrier system and thevarious interconnections between PSAG 115 c and mobile applicationsresident on user mobile device clients 53 c. The PSAG 115 c is shown ashaving an interface to the core network elements and Serving GPRSSupport Nodes (SGSNs) 150 a and the LTE Radio Access Network (RAN) 52 a.The PSAG 115 c is also shown as a front end processor to the GGSN 57 andLTE Gateway 56, which in turn interface with the Internet 54 c tocommunicate with the PSE web and applications servers 55 which may, forexample be housed within public safety entities (PSEs) or otherenterprises or government offices 155. The PSAG 115 c may also have aweb interface 58 to the PSE web and applications servers 55.

FIG. 6 illustrates a functional diagram of an exemplary PSAG 15. Thereis shown an artificial intelligence (AI) engine 60 incorporating deeplearning logic, the LTE and GGSN interfaces 61, low-level applicationprogramming interfaces (APIs) 62 for data collection, storage andprocessing and external high-level APIs 63 for connectivity to PSE weband application servers 55 and the other exemplary servers and DataLakes 42 shown in FIG. 1. There is also a data collector 64 which may,for example, be used for capturing relevant data traffic between firstresponders using mobile applications and PSEs. As such, the datacollector 64 may provide a central point of continuous, event-drivenand/or query-driven data acquisition and delivery models on behalf ofand among a plurality of mobile applications and public safety mobiledevices.

PSAG 15 also incorporates a Data Analytics Engine 65 that generatesactionable descriptive analytics 75, diagnostic analytics 76, predictiveanalytics 77, and prescriptive analytics 78, directed to minimizingmobile network and mobile application access latency and response time,maximizing mobile network and mobile application throughput and networkavailability, and optimizing mobile network and mobile applicationend-to-end connectivity for a plurality of voice, data, video, graphics,and text applications directed to Public Safety First Responders.

PSAG 15 would also incorporate a PS Recommendation Engine (PSRE) 66 thatwould capture Public Safety First Responder mobile search requests,identify search patterns by individual users and user groups, storemobile search data, generate mobile search metadata, and performanalytics on the data by interface to the Data Analytics Engine 65 andAI Engine 60. PSRE 66 would provide recommendations in response toPublic Safety mobile search requests, refine recommendations as afunction of iterative mobile search request patterns, and proactivelygenerate recommendations as a function of mobile user and group searchhistory coupled with present in-field circumstances.

In an aspect, PSAG 15 may provide a central point of integration ofpublic safety embedded control data on behalf of a plurality of EvolvedPacket Core (EPC) network elements including for example, Packet Gateway(PGW), Home Subscriber Server (HSS), Mobility Management Entity (MME),Diameter Routing Agent (DRA), Domain Name Server (DNS), Virtual PrivateNetwork (VPN) router, Subscriber Profile and Policy Repository (SPR),Policy and Charging Rules Function (PCRF), Policy and ChargingEnforcement Function (PCEF), Security Gateway (SeGW), SystemArchitecture Evolution-Gateway (SAE-GW).

In an aspect, PSAG 15 may provide a central point of integration ofpublic safety embedded control data on behalf of a plurality ofUniversal Signaling Platform (USP) network elements including forexample, HSS, DRA, DNS, Electronic Numbering (ENUM), Session BorderController (SBC), Lawful Intercept Gateway (LIG), Border Gateway ControlFunction (BGCF) and Internet Protocol Multimedia System (IM).

In an aspect, PSAG 15 may provide a central point of integration ofpublic safety embedded control data on behalf of a plurality ofdistributed Radio Access Network (RAN) elements including for example,Baseband Unit (BBU), Remote Radio Unit (RRU), evolved Node B (eNodeB,eNB), and Multi-Operator Core Network (MOCN).

In an aspect, PSAG 15 may provide a central point of integration ofembedded control data on behalf of a plurality of public safetyapplication servers.

PSAG 15 may be implemented as a general purpose computer programmed toprovide the functions set forth above, and as such, may have a CPUfunction 67 and a memory 69 for storing executable instructions thereon.

FIG. 7 shows an exemplary functional description of the high-level APIsand the low level APIs associated with PSAG 15. The high-level APIs may,for example, interface with external PSEs, Enterprise customers andcarrier customers APIs which may, for example, interface with PSAG datacollectors, storage and processing functions 142 either internal to thePSAG 15 or external to PSAG 15. With these various high level andlow-level APIs and with its position in the carrier network 14 andaccess to various application servers, PSAG 15 may provide a centralpoint of dynamic, software-based, optimized data-centric, hierarchical,and location-based routing and switching functions within and among aplurality of public safety mobile applications and public safety mobiledevices and to external, non-PSAG software-defined components.

Software Defined Network Implementation.

In an aspect, the PSAG functionality may be implemented through softwaredefined network (SDN) functioning as a virtual network function (VNF)operating on a virtual machine (VM). In an aspect, a software imagehaving executable programming code embedded therein may be loaded onto ahardware platform having memory, a central processing unit (CPU) thatmay be configured as a virtual CPU, a network interface, and otherresources. A hardware platform may serve as a host to one or VMsconfigured to perform the PSAG functionality.

In an SDN implementation, the data repository 42 may be cloud-based suchthat storage may be dynamically allocated based on traffic, processingrequirements or other criteria. In a cloud based data repository 42,PSEs and other entities may be granted access to the data repository 42for analysis or any other purpose. Additionally, cloud-based datarepositories 42 may facilitate the interaction among and betweenmultiple carriers, thereby increasing both the quantity and quality ofthe data and subsequently permit the emergency responses to be moretimely and efficient.

The PSAG functionality may be deployed in geographically diverse regionsto minimize latency and other network delays. An SDN configuration mayfacilitate such deployments. There may also be different applicationstargeted for various instances of the VM. For example, there may belocal PSAG instantiations in an SDN implementation that only collect andmanage data from local events like traffic accidents and 911 ambulancecalls and then feed that data into the data repository 42. There may beother instantiations of a PSAG that are targeted for natural disasterssuch as floods, tornadoes, hurricanes or earthquakes, as well as aplurality of manmade disaster scenarios. This may allow the virtualresources to be used for other functionality when such natural disastersor manmade disaster events are not occurring, thereby allowing thenetwork to operate more efficiently.

In an SDN implementation, a VNF may be directed to a PSAG mediator andone or more VNFs may be directed to the PSAG itself. As such, the PSAGmediator may operate on the same hardware platform as a PSAG or on adifferent platform apart from the one or more PSAGs.

Data Collection and Storage.

In an aspect, as a front end processor for either an LTE gateway or aGGSN, one or more PSAGs 15 may continually be monitoring, analyzing andstoring emergency communications data. Referring back to FIG. 6, thefront end processor (FEP) 61 function within PSAG 15 may provide asimple pass-through to the LTE gateway or GGSN for data that is notassociated with emergency data traffic. With respect to emergency datatraffic, the front end processor may also provide that emergency datatraffic to the data collector 64 while at the same time passing throughthat emergency data traffic to its intended destination in the network14. The discernment between emergency data traffic and non-emergencydata traffic may be based on metadata, tags, origination or destinationidentification of the data, header information, or any otheridentification method. For example, all data originating from ordirected to a PSE may be routed to the data collector 64. Likewise, alldata originating from or directed mobile applications on user devices offirst responders may be routed to the data collector 64. Sensor data,including data from sensors that monitor emergency supplies and status,for example, and data from sensors that monitor the locations, amountsand temperatures of blood reserves to be used in an emergency situationmay also be routed to the data collector 64 though the internet access18 to the network 14 using IoT functionality.

The PSAG 15 may also request and/or continually or periodicallyautomatically receive data to be stored and aggregated with theemergency data traffic from applications servers within the network 14such as those described with respect to FIG. 1. For example, it isenvisioned that PSAG 15 may be in constant communication and receivingdata from the E911/NG911 application server 38 and the location-basedapplication server 35 to track the location of emergency vehicles andthe user devices of first responders.

Additionally, PSAG 15 may also request and/or continually orperiodically receive data from external servers that may provideadditional relevant data. For example, PSAG 15 may request or receivedata from an external weather server, for example, a weather serverhosted by the National Weather Service to track the path of hurricanes,tornadoes and other severe weather. PSAG 15 may also, for example,collect data from one or more external servers associated withcommercial travel schedules for public transportation, for example,airlines, trains and bus routes and schedules. Other external serverdata may include, but is not limited to, data associated with public andprivate events, including events such as Presidential inaugurations, theWorld Cup soccer tournament, or the Super Bowl that may impact emergencysituations in terms of first responder readiness. Other data may includetraffic data and construction delays during certain hours that mayimpact first responder travel time to an incident.

The AI engine 60 may also adjust the type, amount and time period fordata collection and may do so, for example, based on whether there is anemergency situation and if so, the status of that emergency situation.The AI engine 60 may use, for example, streaming analytics to adjust thedata collection process in real time or near-real time based on the databeing processed.

It will be understood that the data collection function may be performedlocally for a particular geographic area or nationally or a combinationthereof. Data may be collected within a single PSAG 15 or across allPSAGs 15 in a network 14. Data collection may be coordinated acrossmultiple networks using PSAG mediator 145. It will also be understoodthat the above data collector 64 functionality is exemplary only and canbe expanded or restricted in the type, quantity and time periods thatemergency traffic data are collected.

It will also be understood that the data collected may be stored atleast temporarily locally with in the PSAG 15 or stored in datarepository/Data Lakes 42. The data repository/Data Lakes 42 may belocated within network 14 or in the cloud such that multiple parties,service providers and applications may have access to the emergencytraffic data.

Regardless of where the data repository is located, it may be accessedby one or more network access points 11 described in FIG. 1, which mayfor example, include PSTN access 16, Internet access 18, WLAN access 20,wireless access 22, a Wireless Fidelity (WiFi) access point (not shown)or any other access point to the network or to the storage cloud.

With reference to FIG. 8, there is shown an exemplary process forcollection of data in accordance with the present disclosure. At 80, thesources and destination of data to be collected is identified asdescribed above. At 81, any other data to be collected, including datafrom sensors, network and external servers, is identified. At 82, thefrequency and time period for such data collection is set. For example,the frequency and time period for communications to/from firstresponders and PSEs may be continuous, while the frequency of queries toa weather server may be periodically every hour during a 24 hour cycle.At 83, the data are collected and monitored by the PSAG 15, including ananalysis of the data by AI engine 60. At 84, if an emergency situation,either nationally, regionally or locally is detected, then the datacollection parameters may be adjusted at 85 and the data collection andmonitoring is continued at 83. If at 84 there is no emergency situationor if the emergency situation has been resolved, the process repeats at80 and the data to be collected are reset and monitored as shown.

Software Defined Network Detailed Description.

FIG. 14 is a representation of an exemplary network 100. Network 100 maycomprise a Software Defined Network (SDN)—that is, network 100 mayinclude one or more virtualized functions implemented on general purposehardware, such as in lieu of having dedicated hardware for every networkfunction. That is, general purpose hardware of network 100 may beconfigured to run virtual network elements to support communicationservices, such as mobility services, including consumer services andenterprise services. These services may be provided or measured insessions

Virtual network functions (VNFs) 102 may be able to support a limitednumber of sessions. Each VNF 102 may have a VNF type that indicates itsfunctionality or role. For example, FIG. 14 illustrates a gateway VNF102 a and a policy and charging rules function (PCRF) VNF 102 b.Additionally or alternatively, VNFs 102 may include other types of VNFs.Each VNF 102 may use one or more virtual machines (VMs) 106 to operate.Each VM 106 may have a VM type that indicates its functionality or role.For example, FIG. 14 illustrates multiple VMs 106 that may include MCMVM, an ASM VM, and a DEP VM. Additionally or alternatively, VMs 106 mayinclude other types of VMs. Each VM 106 may consume various networkresources from a server 112, such as a resource 108, a virtual centralprocessing unit (vCPU) 108 a, memory 108 b, or a network interface card(NIC) 108 c in FIG. 15. Additionally or alternatively, server 112 mayinclude other types of resources 108.

While FIG. 14 illustrates resources collectively contained in hardwareplatform 911, the configuration of hardware platform 911 may isolate,for example, certain memory 108 b from other memory 108 b FIG. 15provides an exemplary implementation of hardware platform 910.

Hardware platform 911 may comprise one or more chasses 110. Chassis 110may refer to the physical housing or platform for multiple servers 112or other network equipment. In an aspect, chassis 110 may also refer tothe underlying network equipment. Chassis 110 may include one or moreservers 112. Server 112 may comprise general purpose computer hardwareor a computer. In an aspect, chassis 110 may comprise a metal rack, andservers 112 of chassis 110 may comprise blade servers that arephysically mounted in or on chassis 110.

Each server 112 may include one or more network resources 108, asillustrated. Servers 112 may be communicatively coupled together (notshown) in any combination or arrangement. For example, all servers 112within a given chassis 110 may be communicatively coupled. As anotherexample, servers 112 in different chasses 110 may be communicativelycoupled. Additionally or alternatively, chasses 110 may becommunicatively coupled together (not shown) in any combination orarrangement.

The characteristics of each chassis 110 and each server 112 may differ.For example, FIG. 15 illustrates that the number of servers 112 withintwo chasses 110 may vary. Additionally or alternatively, the type ornumber of resources 108 within each server 112 may vary. In an aspect,chassis 110 may be used to group servers 112 with the same resourcecharacteristics. In another aspect, servers 112 within the same chassis110 may have different resource characteristics.

Given hardware platform 911, the number of sessions that may beinstantiated may vary depending upon how efficiently resources 108 areassigned to different VMs 106. For example, assignment of VMs 106 toparticular resources 108 may be constrained by one or more rules. Forexample, a first rule may require that resources 108 assigned to aparticular VM 106 be on the same server 112 or set of servers 112. Forexample, if VM 106 uses eight vCPUs 108 a, 1 GB of memory 108 b, and 2NICs 108 c, the rules may require that all of these resources 108 besourced from the same server 112. Additionally or alternatively, VM 106may require splitting resources 108 among multiple servers 112, but suchsplitting may need to conform with certain restrictions. For example,resources 108 for VM 106 may be able to be split between two servers112. Default rules may apply. For example, a default rule may requirethat all resources 108 for a given VM 106 must come from the same server112.

An affinity rule may restrict assignment of resources 108 for aparticular VM 106 (or a particular type of VM 106). For example, anaffinity rule may require that certain VMs 106 be instantiated on (thatis, consume resources from) the same server 112 or chassis 110. Forexample, if VNF 102 uses six MCM VMs 106, an affinity rule may dictatethat those six MCM VMs 106 be instantiated on the same server 112 (orchassis 110). As another example, if VNF 102 uses MCM VMs 106, ASM VMs106, and a third type of VMs 106, an affinity rule may dictate that atleast the MCM VMs 106 and the ASM VMs 1046 be instantiated on the sameserver 112 (or chassis 110). Affinity rules may restrict assignment ofresources 108 based on the identity or type of resource 108, VNF 102, VM106, chassis 110, server 112, or any combination thereof.

An anti-affinity rule may restrict assignment of resources 108 for aparticular VM 106 (or a particular type of VM 106). In contrast to anaffinity rule—which may require that certain VMs 106 be instantiated onthe same server 112 or chassis 110—an anti-affinity rule requires thatcertain VMs 106 be instantiated on different servers 112 (or differentchasses 110). For example, an anti-affinity rule may require that MCM VM106 be instantiated on a particular server 112 that does not contain anyASM VMs 106. As another example, an anti-affinity rule may require thatMCM VMs 106 for a first VNF 102 be instantiated on a different server112 (or chassis 110) than MCM VMs 106 for a second VNF 102.Anti-affinity rules may restrict assignment of resources 108 based onthe identity or type of resource 108, VNF 102, VM 106, chassis 110,server 112, or any combination thereof.

Within these constraints, resources 108 of servers 112 may be assignedto be used to instantiate VMs 106, which in turn may be used toinstantiate VNFs 102, which in turn may be used to establish sessions.The different combinations for how such resources 108 may be assignedmay vary in complexity and efficiency. For example, differentassignments may have different limits of the number of sessions that canbe established given a particular server 112.

For example, consider a session that may require gateway VNF 102 a andPCRF VNF 102 b. Gateway VNF 102 a may require five VMs 106 instantiatedon the same server 112, and PCRF VNF 102 b may require two VMs 104instantiated on the same server 112. (Assume, for this example, that noaffinity or anti-affinity rules restrict whether VMs 106 for PCRF VNF102 b may or must be instantiated on the same or different server 112than VMs 106 for gateway VNF 102 a.) In this example, each of twoservers 112 may have sufficient resources 108 to support 10 VMs 106. Toimplement sessions using these two servers 112, first server 112 may beinstantiated with 10 VMs 106 to support two instantiations of gatewayVNF 102 a, and second server 112 may be instantiated with 9 VMs: fiveVMs 106 to support one instantiation of gateway VNF 102 a and four VMs106 to support two instantiations of PCRF VNF 102 b. This may leave theremaining resources 108 that could have supported the tenth VM 108 onsecond server 112 unused (and unusable for an instantiation of either agateway VNF 102 a or a PCRF VNF 102 b). Alternatively, first server 112may be instantiated with 10 VMs 106 for two instantiations of gatewayVNF 102 a and second server 112 may be instantiated with 10 VMs 106 forfive instantiations of PCRF VNF 102 b, using all available resources 108to maximize the number of VMs 106 instantiated.

Consider, further, how many sessions each gateway VNF 102 a and eachPCRF VNF 102 b may support. This may factor into which assignment ofresources 108 is more efficient. For example, consider if each gatewayVNF 102 a supports two million sessions, and if each PCRF VNF 102 bsupports three million sessions. For the first configuration—three totalgateway VNFs 102 a (which satisfy the gateway requirement for sixmillion sessions) and two total PCRF VNFs 102 b (which satisfy the PCRFrequirement for six million sessions)—would support a total of sixmillion sessions. For the second configuration—two total gateway VNFs102 a (which satisfy the gateway requirement for four million sessions)and five total PCRF VNFs 102 b (which satisfy the PCRF requirement for15 million sessions)—would support a total of four million sessions.Thus, while the first configuration may seem less efficient looking onlyat the number of available resources 108 used (as resources 108 for thetenth possible VM 106 are unused), the second configuration is actuallymore efficient from the perspective of being the configuration that cansupport the greater number of sessions.

To solve the problem of determining a capacity (or, number of sessions)that can be supported by a given hardware platform 911, a givenrequirement for VNFs 102 to support a session, a capacity for the numberof sessions each VNF 102 (e.g., of a certain type) can support, a givenrequirement for VMs 106 for each VNF 102 (e.g., of a certain type), agiven requirement for resources 108 to support each VM 106 (e.g., of acertain type), rules dictating the assignment of resources 108 to one ormore VMs 106 (e.g., affinity and anti-affinity rules), the chasses 110and servers 112 of hardware platform 911, and the individual resources108 of each chassis 110 or server 112 (e.g., of a certain type), aninteger programming problem may be formulated.

First, a plurality of index sets may be established. For example, indexset L may include the set of chasses 110. For example, if a systemallows up to 6 chasses 110, this set may be:L={1,2,3,4,5,6},where l is an element of L.

Another index set J may include the set of servers 112. For example, ifa system allows up to 16 servers 112 per chassis 110, this set may be:J={1,2,3, . . . ,16},where j is an element of J

As another example, index set K having at least one element k mayinclude the set of VNFs 102 that may be considered. For example, thisindex set may include all types of VNFs 102 that may be used toinstantiate a service. For example, letK={GW,PCRF}where GW represents gateway VNFs 102 a and PCRF represents PCRF VNFs 102b.

Another index set I(k) may equal the set of VMs 106 for a VNF 102 k.Thus, letI(GW)={MCM,ASM,IOM,WSM,CCM,DCM}represent VMs 106 for gateway VNF 102 a, where MCM represents MCM VM106, ASM represents ASM VM 106, and each of IOM, WSM, CCM, and DCMrepresents a respective type of VM 106. Further, letI(PCRF)={DEP,DIR,POL,SES,MAN}represent VMs 106 for PCRF VNF 102 b, where DEP represents DEP VM 106and each of DIR, POL, SES, and MAN represent a respective type of VM106.

Another index set V may include the set of possible instances of a givenVM 104. For example, if a system allows up to 20 instances of VMs 106,this set may be:V={1,2,3, . . . ,20},where v is an element of V.

In addition to the sets, the integer programming problem may includeadditional data. The characteristics of VNFs 102, VMs 106, chasses 110,or servers 112 may be factored into the problem. This data may bereferred to as parameters. For example, for given VNF 102 k, the numberof sessions that VNF 102 k can support may be defined as a functionS(k). In an aspect, for an element k of set K, this parameter may berepresented by S(k)>=0;

as a measurement of the number of sessions k can support. Returning tothe earlier example where gateway VNF 102 a may support 2 millionsessions, then this parameter may be S(GW)=2,000,000.

VM 106 modularity may be another parameter in the integer programmingproblem. VM 106 modularity may represent the VM 106 requirement for atype of VNF 102. For example, for k that is an element of set K and ithat is an element of set I, each instance of VNF k may require M(k, i)instances of VMs 106. For example, recall the example where I(GW)={MCM,ASM, IOM, WSM, CCM, DCM}.

In an example, M(GW, I(GW)) may be the set that indicates the number ofeach type of VM 106 that may be required to instantiate gateway VNF 102a. For example,M(GW,I(GW))={2,16,4,4,2,4}may indicate that one instantiation of gateway VNF 102 a may require twoinstantiations of MCM VMs 106, 16 instantiations of ACM VM 106, fourinstantiations of IOM VM 106, four instantiations of WSM VM 106, twoinstantiations of CCM VM 106, and four instantiations of DCM VM 106.

Another parameter may indicate the capacity of hardware platform 910.For example, a parameter C may indicate the number of vCPUs 108 arequired for each VM 106 type i and for each VNF 102 type k. Forexample, this may include the parameter C(k, i).

For example, if MCM VM 106 for gateway VNF 102 a requires 20 vCPUs 108a, this may be represented asC(GW,MCM)=20.

However, given the complexity of the integer programming problem—thenumerous variables and restrictions that must be satisfied—implementingan algorithm that may be used to solve the integer programming problemefficiently, without sacrificing optimality, may be difficult.

FIG. 16 illustrates a functional block diagram depicting one example ofan LTE-EPS network architecture 400 that may be at least partiallyimplemented as an SDN. Network architecture 400 disclosed herein isreferred to as a modified Long Term Evolution/Evolved Packet System(LTE-EPS) architecture 400 to distinguish it from a traditional LTE-EPSarchitecture.

An example modified LTE-EPS architecture 400 is based at least in parton standards developed by the 3rd Generation Partnership Project (3GPP),with information available at www.3gpp.org. LTE-EPS network architecture400 may include an access network 402, a core network 404, e.g., anEvolved Packet Core (EPC) or Common BackBone (CBB) and one or moreexternal networks 406, sometimes referred to as Packet Data Network(PDN) or peer entities. Different external networks 406 can bedistinguished from each other by a respective network identifier, e.g.,a label according to Domain Name Server (DNS) naming conventionsdescribing an access point to the PDN. Such labels can be referred to asAccess Point Names (APN). External networks 406 can include one or moretrusted and non-trusted external networks such as an internet protocol(IP) network 408, an IP multimedia subsystem (IMS) network 410, andother networks 412, such as a service network, a corporate network, orthe like. In an aspect, access network 402, core network 404, orexternal network 406 may include or communicate with network 100.

Access network 402 can include an LTE network architecture sometimesreferred to as Evolved Universal mobile Telecommunication systemTerrestrial Radio Access (E UTRA) and evolved UMTS Terrestrial RadioAccess Network (E-UTRAN). Broadly, access network 402 can include one ormore communication devices, commonly referred to as User Equipment (UE)414, and one or more wireless access nodes, or base stations 416 a, 416b. During network operations, at least one base station 416 communicatesdirectly with UE 414. Base station 416 can be an evolved Node B(e-NodeB), with which UE 414 communicates over the air and wirelessly.UEs 414 can include, without limitation, wireless devices, e.g.,satellite communication systems, portable digital assistants (PDAs),laptop computers, tablet devices and other mobile devices (e.g.,cellular telephones, smart appliances, and so on). UEs 414 can connectto eNBs 416 when UE 414 is within range according to a correspondingwireless communication technology.

UE 414 generally runs one or more applications that engage in a transferof packets between UE 414 and one or more external networks 406. Suchpacket transfers can include one of downlink packet transfers fromexternal network 406 to UE 414, uplink packet transfers from UE 414 toexternal network 406 or combinations of uplink and downlink packettransfers. Applications can include, without limitation, web browsing,Voice over IP (VoIP), streaming media and the like. Each application canpose different Quality of Service (QoS) requirements on a respectivepacket transfer. Different packet transfers can be served by differentbearers within core network 404, e.g., according to parameters, such asthe QoS.

Core network 404 uses a concept of bearers, e.g., EPS bearers (virtualconnections between UEs and Packet Gateways, PGWs), to route packets,e.g., IP traffic, between a particular gateway in core network 404 andUE 414. A bearer refers generally to an IP packet flow with a definedQoS between the particular gateway and UE 414. Access network 402, e.g.,E UTRAN, and core network 404 together set up and release bearers asrequired by the various applications. Bearers can be classified in atleast two different categories: (i) minimum guaranteed bit rate bearers,e.g., for applications, such as Voice Over IP (VoIP); and (ii)non-guaranteed bit rate bearers that do not require guarantee bit rate,e.g., for applications, such as web browsing.

In one embodiment, the core network 404 includes various networkentities, such as Mobility Management Entity (MME) 418, Serving Gateway(SGW) 420, Home Subscriber Server (HSS) 422, Policy and Charging RulesFunction (PCRF) 424 and Packet Data Network Gateway (PGW) 426. In oneembodiment, MME 418 comprises a control node performing a controlsignaling between various equipment and devices in access network 402and core network 404. The protocols running between UE 414 and corenetwork 404 are generally known as Non-Access Stratum (NAS) protocols.

For illustration purposes only, the terms MME 418, SGW 420, HSS 422 andPGW 426, and so on, can be server devices, but may be referred to in thesubject disclosure without the word “server.” It is also understood thatany form of such servers can operate in a device, system, component, orother form of centralized or distributed hardware and software. It isfurther noted that these terms and other terms such as bearer pathsand/or interfaces are terms that can include features, methodologies,and/or fields that may be described in whole or in part by standardsbodies such as the 3GPP. It is further noted that some or allembodiments of the subject disclosure may in whole or in part modify,supplement, or otherwise supersede final or proposed standards publishedand promulgated by 3GPP.

According to traditional implementations of LTE-EPS architectures, SGW420 routes and forwards all user data packets. SGW 420 also acts as amobility anchor for user plane operation during handovers between basestations, e.g., during a handover from first eNB 416 a to second eNB 416b as may be the result of UE 414 moving from one area of coverage, e.g.,cell, to another. SGW 420 can also terminate a downlink data path, e.g.,from external network 406 to UE 414 in an idle state, and trigger apaging operation when downlink data arrives for UE 414. SGW 420 can alsobe configured to manage and store a context for UE 414, e.g., includingone or more of parameters of the IP bearer service and network internalrouting information. In addition, SGW 420 can perform administrativefunctions, e.g., in a visited network, such as collecting informationfor charging (e.g., the volume of data sent to or received from theuser), and/or replicate user traffic, e.g., to support a lawfulinterception. SGW 420 also serves as the mobility anchor forinterworking with other 3GPP technologies such as universal mobiletelecommunication system (UMTS).

At any given time, UE 414 is generally in one of three different states:detached, idle, or active. The detached state is typically a transitorystate in which UE 414 is powered on but is engaged in a process ofsearching and registering with network 402. In the active state, UE 414is registered with access network 402 and has established a wirelessconnection, e.g., radio resource control (RRC) connection, with eNB 416.Whether UE 414 is in an active state can depend on the state of a packetdata session, and whether there is an active packet data session. In theidle state, UE 414 is generally in a power conservation state in whichUE 414 typically does not communicate packets. When UE 414 is idle, SGW420 can terminate a downlink data path, e.g., from one peer entity 406,and triggers paging of UE 414 when data arrives for UE 414. If UE 414responds to the page, SGW 420 can forward the IP packet to eNB 416 a.

HSS 422 can manage subscription-related information for a user of UE414. For example, HSS 422 can store information such as authorization ofthe user, security requirements for the user, quality of service (QoS)requirements for the user, etc. HSS 422 can also hold information aboutexternal networks 406 to which the user can connect, e.g., in the formof an APN of external networks 406. For example, MME 418 can communicatewith HSS 422 to determine if UE 414 is authorized to establish a call,e.g., a voice over IP (VoIP) call before the call is established.

PCRF 424 can perform QoS management functions and policy control. PCRF424 is responsible for policy control decision-making, as well as forcontrolling the flow-based charging functionalities in a policy controlenforcement function (PCEF), which resides in PGW 426. PCRF 424 providesthe QoS authorization, e.g., QoS class identifier and bit rates thatdecide how a certain data flow will be treated in the PCEF and ensuresthat this is in accordance with the user's subscription profile.

PGW 426 can provide connectivity between the UE 414 and one or more ofthe external networks 406. In illustrative network architecture 400, PGW426 can be responsible for IP address allocation for UE 414, as well asone or more of QoS enforcement and flow-based charging, e.g., accordingto rules from the PCRF 424. PGW 426 is also typically responsible forfiltering downlink user IP packets into the different QoS-based bearers.In at least some embodiments, such filtering can be performed based ontraffic flow templates. PGW 426 can also perform QoS enforcement, e.g.,for guaranteed bit rate bearers. PGW 426 also serves as a mobilityanchor for interworking with non-3GPP technologies such as CDMA2000.

Within access network 402 and core network 404 there may be variousbearer paths/interfaces, e.g., represented by solid lines 428 and 430.Some of the bearer paths can be referred to by a specific label. Forexample, solid line 428 can be considered an S1-U bearer and solid line432 can be considered an S5/S8 bearer according to LTE-EPS architecturestandards. Without limitation, reference to various interfaces, such asS1, X2, S5, S8, S11 refer to EPS interfaces. In some instances, suchinterface designations are combined with a suffix, e.g., a “U” or a “C”to signify whether the interface relates to a “User plane” or a “Controlplane.” In addition, the core network 404 can include various signalingbearer paths/interfaces, e.g., control plane paths/interfacesrepresented by dashed lines 430, 434, 436, and 438. Some of thesignaling bearer paths may be referred to by a specific label. Forexample, dashed line 430 can be considered as an S1-MME signalingbearer, dashed line 434 can be considered as an S11 signaling bearer anddashed line 436 can be considered as an S6a signaling bearer, e.g.,according to LTE-EPS architecture standards. The above bearer paths andsignaling bearer paths are only illustrated as examples and it should benoted that additional bearer paths and signaling bearer paths may existthat are not illustrated.

Also shown is a novel user plane path/interface, referred to as theS1-U+ interface 466. In the illustrative example, the S1-U+ user planeinterface extends between the eNB 416 a and PGW 426. Notably, S1-U+path/interface does not include SGW 420, a node that is otherwiseinstrumental in configuring and/or managing packet forwarding betweeneNB 416 a and one or more external networks 406 by way of PGW 426. Asdisclosed herein, the S1-U+ path/interface facilitates autonomouslearning of peer transport layer addresses by one or more of the networknodes to facilitate a self-configuring of the packet forwarding path. Inparticular, such self-configuring can be accomplished during handoversin most scenarios so as to reduce any extra signaling load on the S/PGWs420, 426 due to excessive handover events.

In some embodiments, PGW 426 is coupled to storage device 440, shown inphantom. Storage device 440 can be integral to one of the network nodes,such as PGW 426, for example, in the form of internal memory and/or diskdrive. It is understood that storage device 440 can include registerssuitable for storing address values. Alternatively or in addition,storage device 440 can be separate from PGW 426, for example, as anexternal hard drive, a flash drive, and/or network storage.

Storage device 440 selectively stores one or more values relevant to theforwarding of packet data. For example, storage device 440 can storeidentities and/or addresses of network entities, such as any of networknodes 418, 420, 422, 424, and 426, eNBs 416 and/or UE 414. In theillustrative example, storage device 440 includes a first storagelocation 442 and a second storage location 444. First storage location442 can be dedicated to storing a Currently Used Downlink address value442. Likewise, second storage location 444 can be dedicated to storing aDefault Downlink Forwarding address value 444. PGW 426 can read and/orwrite values into either of storage locations 442, 444, for example,managing Currently Used Downlink Forwarding address value 442 andDefault Downlink Forwarding address value 444 as disclosed herein.

In some embodiments, the Default Downlink Forwarding address for eachEPS bearer is the SGW S5-U address for each EPS Bearer. The “CurrentlyUsed Downlink Forwarding address” for each EPS bearer in PGW 426 can beset every time when PGW 426 receives an uplink packet, e.g., a GTP-Uuplink packet, with a new source address for a corresponding EPS bearer.When UE 414 is in an idle state, the “Currently Used Downlink Forwardingaddress” field for each EPS bearer of UE 414 can be set to a “null” orother suitable value.

In some embodiments, the Default Downlink Forwarding address is onlyupdated when PGW 426 receives a new SGW S5-U address in a predeterminedmessage or messages. For example, the Default Downlink Forwardingaddress is only updated when PGW 426 receives one of a Create SessionRequest, Modify Bearer Request and Create Bearer Response messages fromSGW 420.

As values 442, 444 can be maintained and otherwise manipulated on a perbearer basis, it is understood that the storage locations can take theform of tables, spreadsheets, lists, and/or other data structuresgenerally well understood and suitable for maintaining and/or otherwisemanipulate forwarding addresses on a per bearer basis.

It should be noted that access network 402 and core network 404 areillustrated in a simplified block diagram in FIG. 16. In other words,either or both of access network 402 and the core network 404 caninclude additional network elements that are not shown, such as variousrouters, switches and controllers. In addition, although FIG. 16illustrates only a single one of each of the various network elements,it should be noted that access network 402 and core network 404 caninclude any number of the various network elements. For example, corenetwork 404 can include a pool (i.e., more than one) of MMEs 418, SGWs420 or PGWs 426.

In the illustrative example, data traversing a network path between UE414, eNB 416 a, SGW 420, PGW 426 and external network 406 may beconsidered to constitute data transferred according to an end-to-end IPservice. However, for the present disclosure, to properly performestablishment management in LTE-EPS network architecture 400, the corenetwork, data bearer portion of the end-to-end IP service is analyzed.

An establishment may be defined herein as a connection set up requestbetween any two elements within LTE-EPS network architecture 400. Theconnection set up request may be for user data or for signaling. Afailed establishment may be defined as a connection set up request thatwas unsuccessful. A successful establishment may be defined as aconnection set up request that was successful.

In one embodiment, a data bearer portion comprises a first portion(e.g., a data radio bearer 446) between UE 414 and eNB 416 a, a secondportion (e.g., an S1 data bearer 428) between eNB 416 a and SGW 420, anda third portion (e.g., an S5/S8 bearer 432) between SGW 420 and PGW 426.Various signaling bearer portions are also illustrated in FIG. 16. Forexample, a first signaling portion (e.g., a signaling radio bearer 448)between UE 414 and eNB 416 a, and a second signaling portion (e.g., S1signaling bearer 430) between eNB 416 a and MME 418.

In at least some embodiments, the data bearer can include tunneling,e.g., IP tunneling, by which data packets can be forwarded in anencapsulated manner, between tunnel endpoints. Tunnels, or tunnelconnections can be identified in one or more nodes of network 100, e.g.,by one or more of tunnel endpoint identifiers, an IP address and a userdatagram protocol port number. Within a particular tunnel connection,payloads, e.g., packet data, which may or may not include protocolrelated information, are forwarded between tunnel endpoints.

An example of first tunnel solution 450 includes a first tunnel 452 abetween two tunnel endpoints 454 a and 456 a, and a second tunnel 452 bbetween two tunnel endpoints 454 b and 456 b. In the illustrativeexample, first tunnel 452 a is established between evolved Node B(eNodeB, eNB) 416 a and SGW 420. Accordingly, first tunnel 452 aincludes a first tunnel endpoint 454 a corresponding to an S1-U addressof eNB 416 a (referred to herein as the eNB S1-U address), and secondtunnel endpoint 456 a corresponding to an S1-U address of SGW 420(referred to herein as the SGW S1-U address). Likewise, second tunnel452 b includes first tunnel endpoint 454 b corresponding to an S5-Uaddress of SGW 420 (referred to herein as the SGW S5-U address), andsecond tunnel endpoint 456 b corresponding to an S5-U address of PGW 426(referred to herein as the PGW S5-U address).

In at least some embodiments, first tunnel solution 450 is referred toas a two tunnel solution, e.g., according to the General Packet RadioService (GPRS) Tunneling Protocol User Plane (GTPv1-U based), asdescribed in 3GPP specification TS 29.281, incorporated herein in itsentirety. It is understood that one or more tunnels are permittedbetween each set of tunnel end points. For example, each subscriber canhave one or more tunnels, e.g., one for each Packet Data Protocol (PDP)context that they have active, as well as possibly having separatetunnels for specific connections with different quality of servicerequirements, and so on.

An example of second tunnel solution 458 includes a single or directtunnel 460 between tunnel endpoints 462 and 464. In the illustrativeexample, direct tunnel 460 is established between eNB 416 a and PGW 426,without subjecting packet transfers to processing related to SGW 420.Accordingly, direct tunnel 460 includes first tunnel endpoint 462corresponding to the eNB S1-U address, and second tunnel endpoint 464corresponding to the PGW S5-U address. Packet data received at eitherend can be encapsulated into a payload and directed to the correspondingaddress of the other end of the tunnel. Such direct tunneling avoidsprocessing, e.g., by SGW 420 that would otherwise relay packets betweenthe same two endpoints, e.g., according to a protocol, such as the GTP-Uprotocol.

In some scenarios, direct tunneling solution 458 can forward user planedata packets between eNB 416 a and PGW 426, by way of SGW 420. That is,SGW 420 can serve a relay function, by relaying packets between twotunnel endpoints 416 a, 426. In other scenarios, direct tunnelingsolution 458 can forward user data packets between eNB 416 a and PGW426, by way of the S1 U+ interface 466, thereby bypassing SGW 420.

Generally, UE 414 can have one or more bearers at any one time. Thenumber and types of bearers can depend on applications, defaultrequirements, and so on. It is understood that the techniques disclosedherein, including the configuration, management and use of varioustunnel solutions 450, 458, can be applied to the bearers on anindividual bases. That is, if user data packets of one bearer, say abearer associated with a VoIP service of UE 414, then the forwarding ofall packets of that bearer are handled in a similar manner. Continuingwith this example, the same UE 414 can have another bearer associatedwith it through the same eNB 416 a. This other bearer, for example, canbe associated with a relatively low rate data session forwarding userdata packets through core network 404 simultaneously with the firstbearer. Likewise, the user data packets of the other bearer are alsohandled in a similar manner, without necessarily following a forwardingpath or solution of the first bearer. Thus, one of the bearers may beforwarded through direct tunnel 458; whereas, another one of the bearersmay be forwarded through a two-tunnel solution 450.

FIG. 17 depicts an exemplary diagrammatic representation of a machine inthe form of a computer system 500 within which a set of instructions,when executed, may cause the machine to perform any one or more of themethods described above. One or more instances of the machine canoperate, for example, as processor 504 for UE 414, eNB 416, MME 418, SGW420, HSS 422, PCRF 424, PGW 426 and other devices. In some embodiments,the machine may be connected (e.g., using a network 502) to othermachines. In a networked deployment, the machine may operate in thecapacity of a server or a client user machine in a server-client usernetwork environment, or as a peer machine in a peer-to-peer (ordistributed) network environment.

The machine may comprise a server computer, a client user computer, apersonal computer (PC), a tablet, a smart phone, a laptop computer, adesktop computer, a control system, a network router, switch or bridge,or any machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. It will beunderstood that a communication device of the subject disclosureincludes broadly any electronic device that provides voice, video ordata communication. Further, while a single machine is illustrated, theterm “machine” shall also be taken to include any collection of machinesthat individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methods discussed herein.

Computer system 500 may include a processor (or controller) 504 (e.g., acentral processing unit (CPU)), a graphics processing unit (GPU, orboth), a main memory 506 and a static memory 508, which communicate witheach other via a bus 510. The computer system 500 may further include adisplay unit 512 (e.g., a liquid crystal display (LCD), a flat panel, ora solid state display). Computer system 500 may include an input device514 (e.g., a keyboard), a cursor control device 516 (e.g., a mouse), adisk drive unit 518, a signal generation device 520 (e.g., a speaker orremote control) and a network interface device 522. In distributedenvironments, the embodiments described in the subject disclosure can beadapted to utilize multiple display units 512 controlled by two or morecomputer systems 500. In this configuration, presentations described bythe subject disclosure may in part be shown in a first of display units512, while the remaining portion is presented in a second of displayunits 512.

The disk drive unit 518 may include a tangible computer-readable storagemedium 518 having instructions 524 on which is stored one or more setsof instructions (e.g., software 526) embodying any one or more of themethods or functions described herein, including those methodsillustrated above. Instructions 526 may also reside, completely or atleast partially, within main memory 506, static memory 508, or withinprocessor 504 during execution thereof by the computer system 500. Mainmemory 506 and processor 504 also may constitute tangiblecomputer-readable storage media.

As shown in FIG. 18, telecommunication system 600 may include wirelesstransmit/receive units (WTRUs) 602, a Radio Access Network (RAN) 604, acore network 606, a public switched telephone network (PSTN) 608, theInternet 610, or other networks 612, though it will be appreciated thatthe disclosed examples contemplate any number of WirelessTransmit/Receive Units (WTRUs), base stations, networks, or networkelements. Each WTRU 602 may be any type of device configured to operateor communicate in a wireless environment. For example, a WTRU maycomprise a mobile device, network device or the like, or any combinationthereof. By way of example, WTRUs 602 may be configured to transmit orreceive wireless signals and may include a UE, a mobile station, amobile device, a fixed or mobile subscriber unit, a pager, a cellulartelephone, a PDA, a smartphone, a laptop, a netbook, a personalcomputer, a wireless sensor, consumer electronics, or the like. WTRUs602 may be configured to transmit or receive wireless signals over anair interface 614.

Telecommunication system 600 may also include one or more base stations616. Each of base stations 616 may be any type of device configured towirelessly interface with at least one of the WTRUs 602 to facilitateaccess to one or more communication networks, such as core network 606,PTSN 608, Internet 610, or other networks 612. By way of example, basestations 616 may be a base transceiver station (BTS), a Node-B, an eNodeB, a Home Node B, a Home eNode B, a site controller, an access point(AP), a wireless router, or the like. While base stations 616 are eachdepicted as a single element, it will be appreciated that base stations616 may include any number of interconnected base stations or networkelements.

RAN 604 may include one or more base stations 616, along with othernetwork elements (not shown), such as a base station controller (BSC), aradio network controller (RNC), or relay nodes. One or more basestations 616 may be configured to transmit or receive wireless signalswithin a particular geographic region, which may be referred to as acell (not shown). The cell may further be divided into cell sectors. Forexample, the cell associated with base station 616 may be divided intothree sectors such that base station 616 may include three transceivers:one for each sector of the cell. In another example, base station 616may employ multiple-input multiple-output (MIMO) technology and,therefore, may utilize multiple transceivers for each sector of thecell.

Base stations 616 may communicate with one or more of WTRUs 602 over airinterface 614, which may be any suitable wireless communication link(e.g., radio frequency (RF), microwave, infrared (IR), ultraviolet (UV),or visible light). Air interface 614 may be established using anysuitable radio access technology (RAT).

More specifically, as noted above, telecommunication system 600 may be amultiple access system and may employ one or more channel accessschemes, such as Code Division Multiple Access (CDMA), Time-DivisionMultiple Access (TDMA), Frequency Division Multiple Access (FDMA),Orthogonal FDMA (OFDMA), Single-Carrier FDMA (SC-FDMA), or the like. Forexample, base station 616 in RAN 604 and WTRUs 602 connected to RAN 604may implement a radio technology such as Universal MobileTelecommunications System (UMTS) Terrestrial Radio Access (UTRA) thatmay establish air interface 614 using wideband CDMA (WCDMA). WCDMA mayinclude communication protocols, such as High-Speed Packet Access (HSPA)or Evolved HSPA (HSPA+). HSPA may include High-Speed Downlink PacketAccess (HSDPA) or High-Speed Uplink Packet Access (HSUPA).

As another example base station 616 and WTRUs 602 that are connected toRAN 604 may implement a radio technology such as Evolved UMTSTerrestrial Radio Access (E-UTRA), which may establish air interface 614using LTE or LTE-Advanced (LTE-A).

Optionally base station 616 and WTRUs 602 connected to RAN 604 mayimplement radio technologies such as IEEE 802.16 (i.e., WorldwideInteroperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1×,CDMA2000 Evolution-Data Optimized (EV-DO), Interim Standard 2000(IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856),GSM, Enhanced Data rates for GSM Evolution (EDGE), Global System forMobile Communication (GSM) EDGE (GERAN), or the like.

Base station 616 may be a wireless router, Home Node B, Home eNode B, oraccess point, for example, and may utilize any suitable RAT forfacilitating wireless connectivity in a localized area, such as a placeof business, a home, a vehicle, a campus, or the like. For example, basestation 616 and associated WTRUs 602 may implement a radio technologysuch as IEEE 802.11 to establish a wireless local area network (WLAN).As another example, base station 616 and associated WTRUs 602 mayimplement a radio technology such as IEEE 802.15 to establish a wirelesspersonal area network (WPAN). In yet another example, base station 616and associated WTRUs 602 may utilize a cellular-based RAT (e.g., WCDMA,CDMA2000, GSM, LTE, LTE-A, etc.) to establish a picocell or femtocell.As shown in FIG. 18, base station 616 may have a direct connection toInternet 610. Thus, base station 616 may not be required to accessInternet 610 via core network 606.

RAN 604 may be in communication with core network 606, which may be anytype of network configured to provide voice, data, applications, and/orvoice over internet protocol (VoIP) services to one or more WTRUs 602.For example, core network 606 may provide call control, billingservices, mobile location-based services, pre-paid calling, Internetconnectivity, video distribution or high-level security functions, suchas user authentication. Although not shown in FIG. 18, it will beappreciated that RAN 604 or core network 606 may be in direct orindirect communication with other RANs that employ the same RAT as RAN604 or a different RAT. For example, in addition to being connected toRAN 604, which may be utilizing an E-UTRA radio technology, core network606 may also be in communication with another RAN (not shown) employinga GSM radio technology.

Core network 606 may also serve as a gateway for WTRUs 602 to accessPSTN 608, Internet 610, or other networks 612. PSTN 608 may includecircuit-switched telephone networks that provide plain old telephoneservice (POTS). For LTE core networks, core network 606 may use IMS core606 to provide access to PSTN 608. Internet 610 may include a globalsystem of interconnected computer networks or devices that use commoncommunication protocols, such as the transmission control protocol(TCP), user datagram protocol (UDP), or Internet Protocol (IP) in theTCP/IP internet protocol suite. Other networks 612 may include wired orwireless communications networks owned or operated by other serviceproviders. For example, other networks 612 may include another corenetwork connected to one or more RANs, which may employ the same RAT asRAN 604 or a different RAT.

Some or all WTRUs 602 in telecommunication system 600 may includemulti-mode capabilities. That is, WTRUs 602 may include multipletransceivers for communicating with different wireless networks overdifferent wireless links. For example, one or more WTRUs 602 may beconfigured to communicate with base station 616, which may employ acellular-based radio technology, and with base station 616, which mayemploy an IEEE 802 radio technology.

FIG. 19 is an example system 700 including RAN 604 and core network 606.As noted above, RAN 604 may employ an E-UTRA radio technology tocommunicate with WTRUs 602 over air interface 614. RAN 604 may also bein communication with core network 606.

RAN 604 may include any number of eNode-Bs 702 while remainingconsistent with the disclosed technology. One or more eNode-Bs 702 mayinclude one or more transceivers for communicating with the WTRUs 602over air interface 614. Optionally, eNode-Bs 702 may implement MIMOtechnology. Thus, one of eNode-Bs 702, for example, may use multipleantennas to transmit wireless signals to, or receive wireless signalsfrom, one of WTRUs 602.

Each of eNode-Bs 702 may be associated with a particular cell (notshown) and may be configured to handle radio resource managementdecisions, handover decisions, scheduling of users in the uplink ordownlink, or the like. As shown in FIG. 19 eNode-Bs 702 may communicatewith one another over an X2 interface.

Core network 606 shown in FIG. 19 may include a mobility managementgateway or entity (MME) 704, a serving gateway 706, or a packet datanetwork (PDN) gateway 708. While each of the foregoing elements aredepicted as part of core network 606, it will be appreciated that anyone of these elements may be owned or operated by an entity other thanthe core network operator.

MME 704 may be connected to each of eNode-Bs 702 in RAN 604 via an S1interface and may serve as a control node. For example, MME 704 may beresponsible for authenticating users of WTRUs 602, bearer activation ordeactivation, selecting a particular serving gateway during an initialattach of WTRUs 602, or the like. MME 704 may also provide a controlplane function for switching between RAN 604 and other RANs (not shown)that employ other radio technologies, such as GSM or Wideband CDMA(WCDMA).

Serving gateway 706 may be connected to each of eNode-Bs 702 in RAN 604via the S1 interface. Serving gateway 706 may generally route or forwarduser data packets to or from the WTRUs 602. Serving gateway 706 may alsoperform other functions, such as anchoring user planes duringinter-eNode B handovers, triggering paging when downlink data isavailable for WTRUs 602, managing or storing contexts of WTRUs 602, orthe like.

Serving gateway 706 may also be connected to PDN gateway 708, which mayprovide WTRUs 602 with access to packet-switched networks, such asInternet 610, to facilitate communications between WTRUs 602 andIP-enabled devices.

Core network 606 may facilitate communications with other networks. Forexample, core network 606 may provide WTRUs 602 with access tocircuit-switched networks, such as PSTN 608, such as through IMS core606 to facilitate communications between WTRUs 602 and traditionalland-line communications devices. In addition, core network 606 mayprovide the WTRUs 602 with access to other networks 612, which mayinclude other wired or wireless networks that are owned or operated byother service providers.

FIG. 20 depicts an overall block diagram of an example packet-basedmobile cellular network environment, such as a GPRS network as describedherein. In the example packet-based mobile cellular network environmentshown in FIG. 20, there are a plurality of base station subsystems (BSS)800 (only one is shown), each of which comprises a base stationcontroller (BSC) 802 serving a plurality of Baseband TransceiverStations (BTSs), such as BTSs 804, 806, 808. BTSs 804, 806, 808 are theaccess points where users of packet-based mobile devices becomeconnected to the wireless network. In example fashion, the packettraffic originating from mobile devices is transported via anover-the-air interface to BTS 808, and from BTS 808 to BSC 802. Basestation subsystems, such as BSS 800, are a part of internal frame relaynetwork 810 that can include a Serving GPRS Support Node (SGSN), such asSGSN 812 or SGSN 814. Each SGSN 812, 814 is connected to an internalpacket network 816 through which SGSN 812, 814 can route data packets toor from a plurality of gateway GPRS support nodes (GGSN) 818, 820, 822.As illustrated, SGSN 814 and GGSNs 818, 820, 822 are part of internalpacket network 816. GGSNs 818, 820, 822 mainly provide an interface toexternal IP networks such as Public Land Mobile Network (PLMN) 824,corporate intranets/internets 826, or Fixed-End System (FES) or thepublic Internet 828. As illustrated, subscriber corporate network 826may be connected to GGSN 820 via a firewall 830. PLMN 824 may beconnected to GGSN 820 via a border gateway router (BGR) 832. A RemoteAuthentication Dial-In User Service (RADIUS) server 834 may be used forcaller authentication when a user calls corporate network 826.

Generally, there may be a several cell sizes in a network, referred toas macro, micro, pico, femto or umbrella cells. The coverage area ofeach cell is different in different environments. Macro cells can beregarded as cells in which the base station antenna is installed in amast or a building above average roof top level. Micro cells are cellswhose antenna height is under average roof top level. Micro cells aretypically used in urban areas. Pico cells are small cells having adiameter of a few dozen meters. Pico cells are used mainly indoors.Femto cells have the same size as pico cells, but a smaller transportcapacity. Femto cells are used indoors, in residential or small businessenvironments. On the other hand, umbrella cells are used to covershadowed regions of smaller cells and fill in gaps in coverage betweenthose cells.

FIG. 21 illustrates an architecture of a typical GPRS network 900 asdescribed herein. The architecture depicted in FIG. 21 may be segmentedinto four groups: users 902, RAN 904, core network 906, and interconnectnetwork 908. Users 902 comprise a plurality of end users, who each mayuse one or more devices 910. Note that device 910 is referred to as amobile subscriber (MS) in the description of network shown in FIG. 21.In an example, device 910 comprises a communications device (e.g.,mobile device 102, mobile positioning center 116, network device 300,any of detected devices 500, second device 508, access device 604,access device 606, access device 608, access device 610 or the like, orany combination thereof). Radio access network 904 comprises a pluralityof BSSs such as BSS 912, which includes a BTS 914 and a BSC 916. Corenetwork 906 may include a host of various network elements. Asillustrated in FIG. 21, core network 906 may comprise MSC 918, servicecontrol point (SCP) 920, gateway MSC (GMSC) 922, SGSN 924, home locationregister (HLR) 926, authentication center (AuC) 928, domain name system(DNS) server 930, and GGSN 932. Interconnect network 908 may alsocomprise a host of various networks or other network elements. Asillustrated in FIG. 21, interconnect network 908 comprises a PSTN 934,an FES/Internet 936, firewall 938, or a corporate network 940.

An MSC can be connected to a large number of BSCs. At MSC 918, forinstance, depending on the type of traffic, the traffic may be separatedin that voice may be sent to PSTN 934 through GMSC 922, or data may besent to SGSN 924, which then sends the data traffic to GGSN 932 forfurther forwarding.

When MSC 918 receives call traffic, for example, from BSC 916, it sendsa query to a database hosted by SCP 920, which processes the request andissues a response to MSC 918 so that it may continue call processing asappropriate.

Home Location Register (HLR) 926 is a centralized database for users toregister to the GPRS network. HLR 926 stores static information aboutthe subscribers such as the International Mobile Subscriber Identity(IMSI), subscribed services, or a key for authenticating the subscriber.HLR 926 also stores dynamic subscriber information such as the currentlocation of the MS. Associated with HLR 926 is AuC 928, which is adatabase that contains the algorithms for authenticating subscribers andincludes the associated keys for encryption to safeguard the user inputfor authentication.

In the following, depending on context, “mobile subscriber” or “MS”sometimes refers to the end user and sometimes to the actual portabledevice, such as a mobile device, used by an end user of the mobilecellular service. When a mobile subscriber turns on his or her mobiledevice, the mobile device goes through an attach process by which themobile device attaches to an SGSN of the GPRS network. In FIG. 21, whenMS 910 initiates the attach process by turning on the networkcapabilities of the mobile device, an attach request is sent by MS 910to SGSN 924. The SGSN 924 queries another SGSN, to which MS 910 wasattached before, for the identity of MS 910. Upon receiving the identityof MS 910 from the other SGSN, SGSN 924 requests more information fromMS 910. This information is used to authenticate MS 910 together withthe information provided by HLR 926. Once verified, SGSN 924 sends alocation update to HLR 926 indicating the change of location to a newSGSN, in this case SGSN 924. HLR 926 notifies the old SGSN, to which MS910 was attached before, to cancel the location process for MS 910. HLR926 then notifies SGSN 924 that the location update has been performed.At this time, SGSN 924 sends an Attach Accept message to MS 910, whichin turn sends an Attach Complete message to SGSN 924.

Next, MS 910 establishes a user session with the destination network,corporate network 940, by going through a Packet Data Protocol (PDP)activation process. Briefly, in the process, MS 910 requests access tothe Access Point Name (APN), for example, UPS.com, and SGSN 924 receivesthe activation request from MS 910. SGSN 924 then initiates a DNS queryto learn which GGSN 932 has access to the UPS.com APN. The DNS query issent to a DNS server within core network 906, such as DNS server 930,which is provisioned to map to one or more GGSNs in core network 906.Based on the APN, the mapped GGSN 932 can access requested corporatenetwork 940. SGSN 924 then sends to GGSN 932 a Create PDP ContextRequest message that contains necessary information. GGSN 932 sends aCreate PDP Context Response message to SGSN 924, which then sends anActivate PDP Context Accept message to MS 910.

Once activated, data packets of the call made by MS 910 can then gothrough RAN 904, core network 906, and interconnect network 908, in aparticular FES/Internet 936 and firewall 938, to reach corporate network940.

FIG. 22 illustrates a PLMN block diagram view of an example architecturethat may be replaced by a telecommunications system. In FIG. 22, solidlines may represent user traffic signals, and dashed lines may representsupport signaling. MS 1002 is the physical equipment used by the PLMNsubscriber. For example, network device or the like may serve as MS1002. MS 1002 may be one of, but not limited to, a cellular telephone, acellular telephone in combination with another electronic device or anyother wireless mobile communication device.

MS 1002 may communicate wirelessly with BSS 1004. BSS 1004 contains BSC1006 and a BTS 1008. BSS 1004 may include a single BSC 1006/BTS 1008pair (base station) or a system of BSC/BTS pairs that are part of alarger network. BSS 1004 is responsible for communicating with MS 1002and may support one or more cells. BSS 1004 is responsible for handlingcellular traffic and signaling between MS 1002 and a core network 1010.Typically, BSS 1004 performs functions that include, but are not limitedto, digital conversion of speech channels, allocation of channels tomobile devices, paging, or transmission/reception of cellular signals.

Additionally, MS 1002 may communicate wirelessly with RNS 1012. RNS 1012contains a Radio Network Controller (RNC) 1014 and one or more Nodes B1016. RNS 1012 may support one or more cells. RNS 1012 may also includeone or more RNC 1014/Node B 1016 pairs or alternatively a single RNC1014 may manage multiple Nodes B 1016. RNS 1012 is responsible forcommunicating with MS 1002 in its geographically defined area. RNC 1014is responsible for controlling Nodes B 1016 that are connected to it andis a control element in a UMTS radio access network. RNC 1014 performsfunctions such as, but not limited to, load control, packet scheduling,handover control, security functions, or controlling MS 1002 access tocore network 1010.

An E-UTRA Network (E-UTRAN) 1018 is a RAN that provides wireless datacommunications for MS 1002 and UE 1024. E-UTRAN 1018 provides higherdata rates than traditional UMTS. It is part of the LTE upgrade formobile networks, and later releases meet the requirements of theInternational Mobile Telecommunications (IMT) Advanced and are commonlyknown as a 4G networks. E-UTRAN 1018 may include of series of logicalnetwork components such as E-UTRAN Node B (eNB) 1020 and E-UTRAN Node B(eNB) 1022. E-UTRAN 1018 may contain one or more eNBs. User equipment(UE) 1024 may be any mobile device capable of connecting to E-UTRAN 1018including, but not limited to, a personal computer, laptop, mobiledevice, wireless router, or other device capable of wirelessconnectivity to E-UTRAN 1018. The improved performance of the E-UTRAN1018 relative to a typical UMTS network allows for increased bandwidth,spectral efficiency, and functionality including, but not limited to,voice, high-speed applications, large data transfer or IPTV, while stillallowing for full mobility.

Typically MS 1002 may communicate with any or all of BSS 1004, RNS 1012,or E-UTRAN 1018. In an illustrative system, each of BSS 1004, RNS 1012,and E-UTRAN 1018 may provide MS 1002 with access to core network 1010.Core network 1010 may include of a series of devices that route data andcommunications between end users. Core network 1010 may provide networkservice functions to users in the circuit switched (CS) domain or thepacket switched (PS) domain. The CS domain refers to connections inwhich dedicated network resources are allocated at the time ofconnection establishment and then released when the connection isterminated. The PS domain refers to communications and data transfersthat make use of autonomous groupings of bits called packets. Eachpacket may be routed, manipulated, processed or handled independently ofall other packets in the PS domain and does not require dedicatednetwork resources.

The circuit-switched Media Gateway (MGW) function (CS-MGW) 1026 is partof core network 1010, and interacts with VLR/MSC server 1028 and GMSCserver 1030 in order to facilitate core network 1010 resource control inthe CS domain. Functions of CS-MGW 1026 include, but are not limited to,media conversion, bearer control, payload processing or other mobilenetwork processing such as handover or anchoring. CS-MGW 1026 mayreceive connections to MS 1002 through BSS 1004 or RNS 1012.

SGSN 1032 stores subscriber data regarding MS 1002 in order tofacilitate network functionality. SGSN 1032 may store subscriptioninformation such as, but not limited to, the IMSI, temporary identities,or PDP addresses. SGSN 1032 may also store location information such as,but not limited to, GGSN address for each GGSN 1034 where an active PDPexists. GGSN 1034 may implement a location register function to storesubscriber data it receives from SGSN 1032 such as subscription orlocation information.

Serving gateway (S-GW) 1036 is an interface which provides connectivitybetween E-UTRAN 1018 and core network 1010. Functions of S-GW 1036include, but are not limited to, packet routing, packet forwarding,transport level packet processing, or user plane mobility anchoring forinter-network mobility. PCRF 1038 uses information gathered from P-GW1036, as well as other sources, to make applicable policy and chargingdecisions related to data flows, network resources or other networkadministration functions. PDN gateway (PDN-GW) 1040 may provideuser-to-services connectivity functionality including, but not limitedto, GPRS/EPC network anchoring, bearer session anchoring and control, orIP address allocation for PS domain connections.

HSS/HLR 1042 is a database for user information and stores subscriptiondata regarding MS 1002 or UE 1024 for handling calls or data sessions.Networks may contain one HSS/HLR 1042 or more if additional resourcesare required. Example data stored by HSS 1042 include, but is notlimited to, user identification, numbering or addressing information,security information, or location information. HSS/HLR 1042 may alsoprovide call or session establishment procedures in both the PS and CSdomains.

VLR/MSC Server 1028 provides user location functionality. When MS 1002enters a new network location, it begins a registration procedure. A MSCserver for that location transfers the location information to the VLRfor the area. A VLR and MSC server may be located in the same computingenvironment, as is shown by VLR/MSC server 1028, or alternatively may belocated in separate computing environments. A VLR may contain, but isnot limited to, user information such as the IMSI, the Temporary MobileStation Identity (TMSI), the Local Mobile Station Identity (LMSI), thelast known location of the mobile station, or the SGSN where the mobilestation was previously registered. The MSC server may containinformation such as, but not limited to, procedures for MS 1002registration or procedures for handover of MS 1002 to a differentsection of core network 1010. Gateway Mobile Services Switching Center(GMSC) server 1030 may serve as a connection to alternate GMSC serversfor other MSs in larger networks.

Equipment Identity Register (EIR) 1044 is a logical element which maystore the IMEI for MS 1002. User equipment may be classified as either“white listed” or “black listed” depending on its status in the network.If MS 1002 is stolen and put to use by an unauthorized user, it may beregistered as “black listed” in EIR 1044, preventing its use on thenetwork. A MME 1046 is a control node which may track MS 1002 or UE 1024if the devices are idle. Additional functionality may include theability of MME 1046 to contact idle MS 1002 or UE 1024 if retransmissionof a previous session is required.

As described herein, a telecommunications system wherein management andcontrol utilizing a software designed network (SDN) and an internetprotocol are based, at least in part, on user equipment, may provide awireless management and control framework that enables common wirelessmanagement and control, such as mobility management, radio resourcemanagement, QoS, load balancing, etc., across many wirelesstechnologies, e.g. LTE, Wi-Fi, and future 5G access technologies;decoupling the mobility control from data planes to let them evolve andscale independently; reducing network state maintained in the networkbased on user equipment types to reduce network cost and allow massivescale; shortening cycle time and improving network upgradability;flexibility in creating end-to-end services based on types of userequipment and applications, thus improve customer experience; orimproving user equipment power efficiency and battery life—especiallyfor simple Machine-to-Machine (M2M) and Internet of Things (IoT)sensors/devices—through enhanced wireless management.

While examples of a telecommunications system in which emergency datacan be processed and managed have been described in connection withvarious computing devices/processors, the underlying concepts may beapplied to any computing device, processor, or system capable offacilitating a telecommunications system. The various techniquesdescribed herein may be implemented in connection with hardware orsoftware or, where appropriate, with a combination of both. Thus, themethods and devices may take the form of program code (i.e.,instructions) embodied in concrete, tangible, storage media having aconcrete, tangible, physical structure. Examples of tangible storagemedia include floppy diskettes, Compact Disc-Read-Only Memory devices(CD-ROMs), Digital Versatile Discs, or, Digital Video Discs (DVDs), harddrives, or any other tangible machine-readable storage medium(computer-readable storage medium). Thus, a computer-readable storagemedium is not a signal. A computer-readable storage medium is not atransient signal. Further, a computer-readable storage medium is not apropagating signal. A computer-readable storage medium as describedherein is an article of manufacture. When the program code is loadedinto and executed by a machine, such as a computer, the machine becomesa device for telecommunications. In the case of program code executionon programmable computers, the computing device will generally include aprocessor, a storage medium readable by the processor (includingvolatile or nonvolatile memory or storage elements), at least one inputdevice, and at least one output device. The program(s) can beimplemented in assembly or machine language, if desired. The languagecan be a compiled or interpreted language, and may be combined withhardware implementations.

The methods and devices associated with a telecommunications system asdescribed herein also may be practiced via communications embodied inthe form of program code that is transmitted over some transmissionmedium, such as over electrical wiring or cabling, through fiber optics,or via any other form of transmission, over the air (OTA), or firmwareover the air (FOTA), wherein, when the program code is received andloaded into and executed by a machine, such as an Erasable ProgrammableRead-Only Memory (EPROM), a gate array, a programmable logic device(PLD), a client computer, or the like, the machine becomes an device forimplementing telecommunications as described herein. When implemented ona general-purpose processor, the program code combines with theprocessor to provide a unique device that operates to invoke thefunctionality of a telecommunications system.

Data Analytics.

In an aspect, PSAG 15 may provide a central point of data correlation onbehalf of and among a plurality of public safety mobile applications andpublic safety mobile devices by collecting and storing relevant data.The data may, for example be stored in data lakes repository 42 as shownin FIG. 1. For the purposes of this disclosure, the exemplary embodimentof data repository 42 will be used, although it will be understood thatthe relevant data may also be stored in a cloud storage environment. Forexample, in an aspect, a plurality of cloud-resident PSAG datarepositories would be accessible by carrier-resident big data analyticsengines, and by PSE and enterprise customer premise-resident big dataapplications, where these data are targeted to PSAG data repositories.The data collected by PSAG 15 and stored in data repository 42 may beaccessible by big data analytics engines.

With reference to FIG. 9, there is shown an exemplary example of how bigdata analytics may be used on the data collected by PSAG 15. There areshown four functional quadrants wherein the horizontal axis shows acontinuum for actionable intelligence and the vertical axis shows acontinuum for key data insights. Within the lower left quadrant, thereis shown a functional box labeled descriptive analytics 75. Generally,descriptive analytics is based on past events and may, for example, bebased on internally generated reports based on the collected data andother business intelligence. Descriptive analytics 75 may generally bedirected to “what happened”.

To analyze “why it happened” the lower right quadrant shows an exemplarydiagnostic analytics function 76. Such a diagnostic analytics functionmay include, for example, the analysis of audio-video data capturedduring an event and similarity-distance analytics, wheresimilarity-distance analytics is generally defined as the “closeness”and similarity of two or more events may also include references toother similar events that happen in the past so as to correlate theprevious diagnostics with the current diagnostic analysis. As such, PSAG15 may generate PSE- and Enterprise-originated diagnostic analytics thatprovide hindsight-based data insights into reasons for prior eventoccurrences.

Moving to the upper left quadrant in FIG. 9, there is shown thepredictive analytics function 77. The predictive analytics function 77is configured to predict what will happen based on the accumulatedhistorical data and the artificial intelligence functions which may beresident in the PSAG 15. The predictive analytics function 77 may, forexample, include exponential down-weighting which, in an aspect, maypreferentially compress data into a single value that can be updatedwithout having to save an entire dataset and in which more recentlyreceived data are weighted more than earlier-received data. Thistypically results in a down-weighting of older data, i.e., the data maydecay over time. Additionally, continuous stream analytics provide thefoundation for fast adaptive actions based on complex event processingand event stream processing, permitting data to be processed before itlands in a database. This predictive analytics function 77 may supportmuch faster decision making than possible with traditional dataanalytics technologies such as descriptive analytics 75 and diagnosticanalytics 76. In other words, the predictive analytics function 77 mayconstantly be calculating statistical analytics while managing andmonitoring live streaming data. Such live streaming data may be pulledfrom a variety of sources by the PSAG 15 and may comprise data generatedby the mobile applications, IoT-based field sensors, PSE communications,and other data being processed by network 14. Data may also includeGlobal Positioning System (GPS) data from mobile devices, firstresponder vehicles, and location of emergency and/or other events.

Finally, with respect to the upper right quadrant of FIG. 9, there isshown a prescriptive analytics function 78 which may, for example,optimize future outcomes in emergency scenarios. The PSAG 15prescriptive analytics elements function 78 may include, for example,embedded analytics in which analytic functions are integrated withinvarious operational processes to improve the efficiency of thoseoperational processes. The prescriptive analytics elements function 78may also include predictive linear regression (PLR) in which an outcomeis predicted based on a change of one of the input variables in terms ofnumeric and categorical inputs (dependent, or response variable), andlogistic regression (LR), in which a binary outcome may be predictedbased on an analysis of the input variables, where a probability ispredicted that an instance belongs to a specific category, for example,the probability that a forecasted hurricane will cause a specific levelof damage. The PLR and LR elements may incorporate nearest-neighborpredictive algorithms (where ‘nearness’ is basic Euclidean distance withresultant utility in selecting non-duplicate variables, rescalevariables, and orthogonalizable variables). By way of further example,prescriptive analytics elements function 78 may also include continuousvariable machine logic (CVML) which may include, for example, otherstatistical based functions including AI-based singular valuedecomposition (SVD), AI-based principal component analysis (PCA), andmetadata. CVML may also include predictive linear regression. Deeplearning functionality in which a cascade of processing layers may beanalyzed layer by layer may also be used in the prescriptive analyticselements function 78. It will be understood that these functions areexemplary only and other statistical methods and functionality may beused to implement the prescriptive analytics function 78.

With reference to FIG. 10a , there is shown an exemplary flow diagram ofprocessing of emergency data that may be performed off-line or innon-real time. Such processing may typically be associated with thedescriptive analytics function 75 to determine what event actuallyhappened, for example, a traffic accident on Route 250 in West Virginiarequiring an ambulance call or a hurricane battering the coast of NorthCarolina requiring the mobilization of various first responders andagencies. This non-real time processing may also be associated with thediagnostic analytics 76 to determine why the event happened, forexample, running a red light in the case of the traffic accident or inthe case of the hurricane, looking at the events in retrospect todetermine why the emergency response was at it was.

The process starts at 90 which involves the capture of emergency data bythe PSAG 15. At 91, any available audio and video streams are captured.At 92, the determination is made as to whether the analytics need to beperformed in real time or may be performed off-line in non-real time.This decision may be made based on the purpose of the analytics. If theanalytics to be performed is meant to analyze the emergency response andto make systemic improvement recommendations, for example, then theanalytics may be performed in non-real time. If the analytics to beperformed is to continually monitor and assess an ongoing emergencysituation, then the analytics may be performed in real time. In thisinstance, it should be understood that real time includes near real timeto allow for the acquisition of processing of incoming data without,however, the storage and subsequent retrieval of the data. If theprocessing may be performed non-real time, then the data is stored forfuture analysis at 93. At 94, the data is subsequently retrieved foranalysis. The timing of such analysis may vary and may, for example, beperformed periodically, at the conclusion of an emergency event, uponcommand of a carrier or a PSE, or on any other schedule in whichprocessing resources may be available to perform the analytics.

At 95, the event is determined. The event determination may, forexample, be a result of analysis of a 911 call or the analysis of sensordata retrieved during an emergency event. The event determination mayalso, for example, be determined based on extrinsic evidentiary datasuch as a police report or a weather report. This analysis is thedescriptive analysis function 75. At 96, diagnostic analysis mayoptionally be performed to determine why the event happened. Forexample, the event may be compared to historical event data using theafore-mentioned similarity-distance analysis. The event may also bediagnosed based on an analysis of the audio and or video captured andwhich may be supplemented by sensor data recorded during the event. Forexample, running the red light in the traffic accident example above mayinclude an analysis of the video to determine which of one or morevehicles were involved in an accident, sensor data from the vehicle todetermine speed, acceleration/deceleration, distance and direction andsensor data from the traffic light to determine the timing of the changeof the signal from green to yellow to red. In any event, the diagnosisis not confined to any one particular method but rather may be adapteddepending on the type of emergency.

In the example of the hurricane, the event may be analyzed/diagnosed notbased on the event itself, but rather based on the response to theevent. For example, data may have been retrieved during the hurricanefrom a server hosted by the National Weather Service which wouldinclude, for example, rainfall amounts, wind speed and direction,reports of destruction, and other data, all time-stamped based on theoccurrence of the event or the receipt of the data by the server. Thesedata may then be analyzed in view of communications between PSEs andfirst responders and sensor data showing the location, speed anddirection of movement of various emergency vehicles. As will beunderstood, other data may also be used to analyze the response to thehurricane event, including data captured by various network servers.

If the data are to be analyzed in real time, the process continues atpoint A in FIG. 10b . In this situation, the AI engine 60 and dataanalytics engine 65 within PSAG 15 may provide the processing analyticsin order to analyze the data in real time or near real time usingcontinuous streaming analytics or other processing methods. This may,for example, result in analyses that predicts future events based on oneor multiple inputs and an historical understanding and statisticalanalysis of events. At 101, the data may be weighted which may, forexample, assign a lower weight to aging data and a higher weight to morerecent data. At 103, the data may be segmented based on time, source,relevance, weight assigned, or any other factor. Once segmented, theweighting function may be reapplies such that the data is weighed basedon the significance of the segment to the particular analysis beingperformed. For example, data associated with the movement of ambulancesand hospital capacity that has been segmented may be given a higherweight than the public comments of officials. At 105, the segments areanalyzed using techniques which may, for example, include continuousstream analytics. At 107, segment outcomes may be predicted and may, forexample, also be assigned a probability, which outcomes are thenassimilated at 109. At 111, the processes may be adjusted based on theprobability of outcomes. For example, if the data analytics shows that ahospital has reached capacity or that a major route to that hospital hasbeen rendered impassable, then the system processes may be adjusted innear real time to inform the first responders to proceed to analternative hospital, even if that alternative hospital is further fromthe current location. In this manner, reactive adjustments to the inputstream of data captured by the PSAG 15 may be made quickly andefficiently.

With reference to FIG. 11, there is shown an exemplary flow diagramillustrating the use of prescriptive analytics. At 113, a processor, forexample, a processor in a PSAG 15, may access stored data from a PSEand/or enterprise and/or first responder communications. From that step,there may be parallel calculations. At 123, real time data are beingcaptured and monitored. At 125, external business or operational systemsmay be accessed and which may, for example, include output fromstatistical modeling of the stored data from 115. At 127, embeddedanalytics may be performed on the data being collected in real time.Embedded analytics may, for example, include the integration of externalbusiness analysis or operational systems such as emergency responsesystems associated with, for example, location, availability, presence,dispatch, and other tools used by PSE and first responders with the flowof real time emergency data. Additional data from external serversincluding, for example, weather services, are also combined to optimizethe predictive analytic outcomes based on the real time data.

In a parallel path, the stored PSE, Enterprise and/or first responderdata may be statistically modelled at 115, including, for example,regression analytics such as predictive linear regression and logisticregression analysis. At 117, analytics based on continuous variablelogic analysis may be performed. This analysis, for example, may predictthe severity of a storm on a continuum of non-destructive to highlydestructive based on historical data analysis. At 119 the real timeembedded analytics described above may be combined with the analyticsbased on stored historical data to optimize outcomes. Because theprocesses may involve deep learning analytics, the process is repeatedat 115 wherein the optimized results are used for further processingwithin each loop of the process.

Reliability, Availability and Latency.

In accordance with the present disclosure, the prescriptive analyticsfunction 78 may be used to increase the reliability and availability ofnetworks and reduce the latency associated with use of such. The PSAG 15predictive and prescriptive analytics elements described above mayproactively and dynamically respond to mobile network access latency andthroughput degradations. While the disclosure will be described withrespect to the PSAG 15, it will be understood that other networkelements may monitor and process the mobile network access latency andperform the throughput analysis.

With reference to FIG. 12, there is shown a graph which illustrates theeffect of a 5% utilization increase over a baseline set of networkutilization percentages, in this example, 50%, 70%, 90%, and 95%baseline utilizations. In this example, utilization (p) ispreferentially sustained utilization (percent busy) of a plurality ofone-to-n, n-to-one, or n-to-n target facility system/operatingenvironments, which may, for example, include application servers, edgesystems, intermediate systems, terrestrial links, celestial links,associated network elements, target backend systems, operatingenvironments, and/or applications. As shown in the graph, an increase(degradation) of 5% from 50% baseline utilization generates a further10% degradation of throughput, an increase (degradation) of 5% from 70%baseline utilization generates a further 18% degradation of throughput,an increase (degradation) of 5% from 90% utilization generates a further91% degradation of throughput, and an increase (degradation) of 5% from95% utilization generates a 2000% further degradation in utilization. Inthese examples, increasingly pronounced throughput degradationsresulting from increasing utilization (p) by 5% from successively higherbaseline utilization levels (i.e., 50%, 70%, 90%, 95%) also adverselyimpact network access latency, throughput, response time, andavailability profiles.

For the purposes of this example, a single-stage, single-server queueingsystem is assumed. Servers may include a plurality of network elementsincluding for example, application servers, edge systems, intermediatesystems, terrestrial links, celestial links, associated networkelements, target backend systems, operating environments, and/orapplications. The expected service time [E(ts)] is expected time inserver (Facility). Expected number of information units per unit of time[E(n)/T)] is calculated as expected traffic load presented to theFacility, including individual, sub-aggregated elements, and aggregatedelements

, expressed as expected number of information units (bits, bytes,blocks), [E(n)] over a unit of time (seconds, minutes, hours). Theassumption is further made that the Facility utilization (ρ) issustained, not sporadic and wherein sustained facility ρ=[E(n)/T×E(ts)]is a sensitive predictor to wait time in queue and is expressed aspercent busy. The expected wait time in queue [E(tw)]=ρ×E(ts)/1ρ, where[E(tw)] predicts expected total time in queueing system. Finally,expected total time in queueing system [E(tq)]=E(ts)+E(tw)], whereexpected total time in queueing system=facility expected servicetime+expected wait time in queue.

Alternatively, PSAG 15 can forward-project degradation in a plurality ofapplication servers, edge systems, intermediate systems, terrestriallinks, celestial links, associated network elements, target backendsystems, operating environments, and/or applications as a function ofincreases (degradations) in sporadic utilization (p).

While the simple case of single-stage single-server queueing model isassumed herein, network facility utilization and resultant delays mayalso be derived for example, based on Markovian/exponential (M)/M/nqueueing systems, G/M/n queueing systems (m-server systems, embeddedMarkov chain), First Come First Served (FCFS) queueing systems, PriorityQueueing systems, Weighted Fair Queueing (WFQ), flow-based andclass-based systems, Modified Weighted Round Robin (MWRR) queueingsystems, and Modified Deficit Round Robin (MDRR) queueing systems.

With the sensitivity to increased network load based on existing networkloading, predictive analytics 77 and prescriptive analytics 78 may beused to minimize network latency and response time, maximize networkthroughput and availability, and optimize end-to-end mobile networkapplication access and communication in the case of an emergencysituation or an event. For example, the PSAG 15 may dynamicallyinterface with the operations support system (OSS) and other networkoperations to monitor in near real time to communicate facility loading.This monitoring may be performed at pre-set intervals by time or may betriggered by facility loading thresholds being exceeded. As such, thePSAG may proactively modify logical topologies and network resourceallocations to maintain latency below a certain threshold and maintainsufficiently high throughput and Quality of Service (QoS), and QoS,Priority and Preemption (QPP) for PSEs and first responders, whereproactive modification of logical topologies and network resourceallocations may include for example, modification of VNF 102 resourceswithin a Software Defined Network (SDN) environment. In this way, PSAG15 detection of near-real-time or projection of near-term KPIthreshold-exceeding network (aggregate and/or element) utilization (p),which in turn, adversely impacts access latency, throughput, responsetime, and availability, and proactively resets topologies through an SDNprocess at VNF 102 for example, in order to ensure optimized networkaccess latency, throughput, response time, and availability on behalf ofpublic safety first responders. Priority, Preemption, and QoS have beendefined in the 3^(rd) Generation Partnership Project (3GPP) sinceRelease 8 (www3gpp.org). QoS mechanisms include for example, MultimediaPriority Services (MPS) identifier, MPS priority, QoS Class Identifier(QCI), Maximum Bit Rate (MBR), Guaranteed Bit Rate (GBR), DifferentiatedServices Code Point (DSCP), traffic flow, Allocation and RetentionPriority (ARP), Access Class (AC), Multi-Access Point Name (APN)support, priority signaling and processing, overload exemption, priorityqueueing, reservation priority, priority bearer preemption, and prioritybearer modification.

In an embodiment, public safety first responder key performanceindicators (KPIs)—for example ARP, QCI, AC, MBR, DSCP—may be pre-set toensure highest priority access and priority retention relative tonon-public safety users potentially or actually occupying a sharedmobile network and network application environment to the extent thatnon-public safety first responder users and traffic may be preemptivelywithheld or dropped from the mobile network and network applicationenvironment to the extent to which non-public safety network trafficbaseline sustained and/or sporadic utilization (p) levels are determinedin advance via PSAG predictive analytics 77 or prescriptive analytics 78to be approaching pre-set trigger thresholds.

In an embodiment, public safety first responder key performanceindicators (KPIs)—for example ARP, QCI, AC, MBR, DSCP—may be pre-set toensure highest priority access and priority retention relative tonon-public safety users occupying a given mobile network and networkapplication environment to the extent that non-public safety firstresponder users and traffic may be preemptively withheld or dropped fromthe mobile network and network application environment to the extent towhich non-public safety network traffic baseline sustained and/orsporadic utilization (p) levels are determined in advance via PSAGpredictive analytics 77 or prescriptive analytics 78 to be approachingtrigger thresholds as a function of PSAG AI Engine 60 and/or PSAGRecommendation Engine 66 elements.

In an embodiment, public safety first responder key performanceindicators (KPIs)—for example ARP, QCI, AC, MBR, DSCP—may be pre-set toensure highest priority access and priority retention relative tonon-public safety users occupying a given mobile network and networkapplication environment. Upon PSAG-detected or PSAG-projected viapredictive analytics 77 or prescriptive analytics 78 that networktraffic baseline sustained and/or sporadic utilization (p) levels aredetermined to be approaching trigger thresholds as a function of PSAG AIEngine 60 and/or PSAG Recommendation Engine 66 elements, a new instanceof SDN-mediated set of Virtual Network Functions (VNFs), VirtualMachines (VM), and related virtual, cloud-based resources may beinstantiated on-demand in order to ensure Public Safety first responderswith sufficiently low access latency and response time, high throughputand availability, and high target application resource access andinteraction, while at the same time not requiring preemption ofpreemptive dropping of non-Public Safety users from network resources.

In an embodiment, the PSAG 15 may also dynamically monitor and maintainsustained and/or sporadic ρ values through deep learning as part of itsprescriptive analytics function 78 and dynamically modify networktopology and resource allocations accordingly to ensure QoS/QPPparameters are maintained for PSEs and first responders.

In an embodiment, the predictive analytics function 77 and prescriptiveanalytics function 78 may be used to predict the occurrence of emergencyevents and to modify network topology and resource allocations inanticipation of increased utilization based on the predicted emergencyevents. For example, if continuous stream analytics shows that anemergency is occurring in a geographic area, normal or routinecommunications traffic in that area may be shifted to another geographiclocation in real time or near-real time to lower the sustainedutilization in the geographic area associated with the emergency inanticipation of the increased utilization by PSEs and first responders.

With reference to FIG. 13, there is shown a flow diagram of an exemplarymethod for modifying network resources based on predictive analytics.The method may, for example, be performed in PSAG 15 or may, in anaspect, be performed by an eNodeB or other network elements. At 131, keyperformance indicator (KPI) thresholds are set. At 133, the baselinenetwork utilization (p) is determined. At decision block 135, it isdetermined whether predictive analytics are projecting a safety event.For example, the predictive analytics using weather data from anexternal server and data analytics may project moderate to severe impactin southeastern Florida. The event may be imminent or it may bepredicted 24 hours in advance. If there is no projected event, theprocess continues at 133 where the baseline network utilization ismonitored. If there is a projected event, the process continues at 137where the network usage is monitored in view of the previously set KPIthresholds. At 139, the KPI measurements are compared to the thresholdsto determine it the KPIs are approaching the thresholds. In thehurricane example, the KPIs may not be approaching the thresholds forthe event projected 24 hours in advance, but as the event comes closer,then the KPIs may be affected and start approaching the thresholds. Ifthe KPIs are not yet approaching the thresholds, then the monitoring ofnetwork usage continues at 137. If the KPIs are approaching thethresholds, then network access for PSEs and first responders may beprioritized at 141. At 143, if the event has not yet concluded, theprocess continues at 137 wherein the KPIs are monitored in view of thenewly prioritized network access and if necessary, furtherprioritization is performed at 141 as the KPIs approach the thresholdsagain. If the event is concluded at 135, the network allocations arereturned to the baseline and that baseline may once again be monitoredat 133.

With respect to 141, the prioritization of network resources may occurin a variety of ways. For example, PSE and first responder networkaccess may simply be given a higher priority which may, for example, beinclude traffic on a dedicated frequency band wherein non-emergencytraffic is moved off of the dedicated frequency band. In an aspect, thenon-emergency traffic may be moved to resources in a differentgeographic area which may increase latency of that non-emergencytraffic, but otherwise may provide both availability and improvedlatency to emergency traffic. In the hurricane example, non-emergencytraffic may be moved to Texas from Florida, freeing up the local Floridaresources for emergency traffic. In another aspect, in a SDN, additionalVMs may be deployed with additional instantiations of the requirednetwork functions to provide those additional network resources ondemand. It will be understood that other ways of prioritization may beincluded and fall within the scope of this disclosure and the appendedclaims.

Additional Material.

The disclosure includes a method comprising accessing, by a processor,data associated with a first emergency event, evaluating, by theprocessor, the data to determine a response to the emergency event byperforming similarity-distance analysis with data associated with aprevious historical emergency event, weighting, by the processor thedata such that data associated with the first emergency event isweighted more than the data associated with the previous emergency eventto determine weighted historical event data, analyzing, by theprocessor, continuous streaming of current data in real time or nearreal time associated with first responder user devices, and optimizing,by the processor, predictions of an upcoming emergency event based onthe analyzing step and the weighted historical event data. Additionally,the method includes wherein the analyzing step includes statisticalanalysis of the current data which may, for example, include regressionanalysis. The method may include wherein the current data is generatedfrom one or more of mobile applications, internet of things fieldsensors, public safety enterprise communications, location of firstresponder user devices, location of first responder vehicles, andlocation of an upcoming emergency event. The optimizing step may includecontinuous variable machine logic analysis of the current data in viewof the weighted historical event data and may further include the stepof prioritizing network access for first responder communications basedon the optimizing step, wherein the prioritizing step comprisesallocating local network resources to first responders in a geographicarea of the third emergency event and allocating non-first respondercommunications to remote resources relative to the geographic area. Inan aspect, the prioritizing step comprises wherein instantiating a newset of SDN-mediated Virtual Network Functions (VNFs) and VirtualMachines (VMs).

The disclosure is also directed to an apparatus comprising aninput-output interface, a processor coupled to the input-outputinterface and wherein the processor is coupled to a memory, the memoryhaving stored thereon executable instructions that when executed by theprocessor cause the processor to effectuate operations comprisingaccessing, by a processor, data associated with a first emergency event,evaluating, by the processor, the data to determine a response to theemergency event by performing similarity-distance analysis with dataassociated with a previous historical emergency event, weighting, by theprocessor the data such that data associated with the first emergencyevent is weighted more than the data associated with the previousemergency event to determine weighted historical event data, analyzing,by the processor, continuous streaming of current data in real time ornear real time associated with first responder user device, andoptimizing, by the processor, predictions of an upcoming emergency eventbased on the analyzing step and the weighted historical event data. Theanalyzing step may include statistical analysis, including regressionanalysis, of the current data. The current data may be generated fromone or more of mobile applications, internet of things field sensors,public safety enterprise communications, location of first responderuser devices, location of first responder vehicles, and location of anupcoming emergency event. The optimizing step may include continuousvariable machine logic analysis of the current data in view of theweighted historical event data. The operations may further includeprioritizing network access for first responder communications based onthe optimizing step wherein the prioritizing step comprises allocatinglocal network resources to first responders in a geographic area of thethird emergency event and allocating non-first responder communicationsto remote resources relative to the geographic area or wherein theprioritizing step includes instantiating a new set of SDN-mediatedVirtual Network Functions (VNFs) and Virtual Machines (VMs).

In an aspect, the disclosure is directed to a method comprisingreceiving, by a processor, emergency data and non-emergency data;discerning, by the processor, the emergency data from the non-emergencydata, forwarding to a gateway by the processor, both the emergency dataand the non-emergency data, analyzing, by the processor, the emergencydata in real time or near real time in view of historical emergencydata; and predicting in part, by the processor, an upcoming emergencyevent based on the analyzing step. The method may further includeprioritizing network access for first responder communications based onthe predicting step and wherein the prioritizing step comprisesallocating local network resources to first responders in a geographicarea of the third emergency event and allocating non-first respondercommunications to remote resources relative to the geographic area orwherein the prioritizing step comprises wherein instantiating a new setof SDN-mediated Virtual Network Functions (VNFs) and Virtual Machines(VMs).

The disclosure is also directed to a method comprising setting keyperformance indicator thresholds for use of a network by public safetyfirst responder devices, determining a baseline network usage profile,projecting a public safety event, monitoring the key performanceindicators in view of the baseline network usage profile andprioritizing network resource allocations for the first responderdevices over network resource allocations for non-first responder basedon the monitoring step. The method may further include estimating theeffect of a projected public safety event on network usage and whereinthe prioritizing step is based on the estimating step. The prioritizingstep may include allocating network resources during the public safetyevent in a geographic area near the public safety event and allocatingresources for non-first responder network access to a differentgeographic area or proactively modifying logical topologies and networkresource allocations to maintain latency below a latency threshold andto maintain throughput above a throughput threshold. The key performanceindicators may be one of Quality of Service (QoS), and QoS, Priority andPreemption (QPP) for public safety enterprises and first responders.Alternatively, the key performance indicators may be one of allocationand retention priority, access class, maximum bit rate, quality ofservice class identifier, and differentiated services code point. Thenetwork facility usage determination may be based on one of a pluralityof queueing models wherein the one of a plurality of queueing models isone of a single stage single service model, a Markovian/exponentialqueueing model, a first come first served queueing model, a priorityqueueing model, a weighted fair queueing model, a modified weightedround robin queueing model and a modified deficit round robin queueingmodel. In an aspect, the monitoring step is performed periodicallyindependently of the projecting step. The method may further includeestimating the effect of a projected public safety event on networkusage and wherein the prioritizing step is based on the estimating step.The monitoring step may be triggered based on projecting step.

In an aspect, the method may further include detecting the public safetyevent and the monitoring step is triggered based on the detecting step.In an aspect, the projecting step may be based on one of predictiveanalytics and prescriptive analytics. The network may be a softwaredefined network (SDN) and the prioritization step comprisesinstantiating a new set of SDN-mediated Virtual Network Functions (VNFs)and Virtual Machines (VMs). The method described above may be performedby one of a radio access network element and a public safety analyticsgateway.

The disclosure is also directed to an apparatus including aninput-output interface, a processor coupled to the input-outputinterface and wherein the processor is coupled to a memory, the memoryhaving stored thereon executable instructions that when executed by theprocessor cause the processor to effectuate operations including settingkey performance indicator thresholds for use of a network by publicsafety first responder devices, determining a baseline network usageprofile, projecting a public safety event, monitoring the keyperformance indicators in view of the baseline network usage profile,and prioritizing network resource allocations for the first responderdevices over network resource allocations for non-first responder basedon the monitoring step. The operations may further include estimatingthe effect of the projected public safety event on network usage andwherein the prioritizing step is based on the estimating step and mayyet further include detecting a public safety event and the monitoringstep is performed based on the detecting step.

In an aspect, the network may be a software defined network (SDN) andthe prioritization step comprises instantiating a new set ofSDN-mediated Virtual Network Functions (VNFs) and Virtual Machines(VMs).

It will be apparent to those skilled in the art that variousmodifications and variations may be made in the present disclosurewithout departing from the scope or spirit of the disclosure. Otheraspects of the disclosure will be apparent to those skilled in the artfrom consideration of the specification and practice of the disclosuredisclosed herein. It is intended that the specification and examples beconsidered as exemplary only, with a true scope and spirit of thedisclosure being indicated by the following claims.

The patentable scope of the disclosure is defined by the claims, and mayinclude other examples that occur to those skilled in the art. Suchother examples are intended to be within the scope of the claims if theyhave structural elements that do not differ from the literal language ofthe claims, or if they include equivalent structural elements withinsubstantial differences from the literal languages of the claims.

The invention claimed is:
 1. A public safety analytics gatewaycomprising: a front end processor configured to communicate with anetwork gateway and a public safety enterprise server; a data collectorin communication with the front end processor; wherein the front endprocessor is configured to receive public safety data from the publicsafety enterprise server and forward the public safety data to both thenetwork gateway and the data collector; a data analytics engine incommunications with the data collector and wherein the data analyticsengine is configured to the analyze public safety data; a recommendationengine in communication with the front end processor, the recommendationengine configured to capture mobile search data associated with a firstresponder user device, store the mobile search data, and generatemetadata associated with the mobile search data; and an artificialintelligence engine in communication with the recommendation engine andthe data analytics engine wherein the artificial intelligence engine isconfigured to store a plurality of records and generate outcomes frominteraction with field-based sensors thereby facilitating predictivealert conditions and response scenarios.
 2. The public safety analyticsgateway of claim 1 further comprising: a recommendation engine incommunication with the front end processor, the recommendation engineconfigured to capture mobile search data associated with a firstresponder user device, store the mobile search data, and generatemetadata associated with the mobile search data.
 3. The public safetyanalytics gateway of claim 1 wherein the data analytics comprises one ofdescriptive analytics, diagnostic analytics, predictive analytics, andprescriptive analytics.
 4. The public safety analytics gateway of claim3 wherein the data analytics is used to monitor mobile application atleast one of access latency, throughput, response time, and availabilitywherein mobile access applications comprise one of voice, data, video,graphics, and text applications for first responders.
 5. The publicsafety analytics gateway of claim 1 wherein the apparatus is incommunication with external servers.
 6. The public safety analyticsgateway of claim 1 wherein the apparatus is configured to communicatewith a second public safety analytics gateway apparatus in a network. 7.The public safety analytics gateway of claim 1 wherein the apparatusprovides a central point of integration of public safety embeddedcontrol data on behalf of a plurality of distributed Radio AccessNetwork (RAN) elements.
 8. An apparatus comprising: an input-outputinterface; a processor coupled to the input-output interface and whereinthe processor is coupled to a memory, the memory having stored thereonexecutable instructions that when executed by the processor cause theprocessor to effectuate operations comprising: receiving, by a publicsafety analytics gateway having an artificial intelligence engine, dataassociated with public safety and data not associated with publicsafety; discerning, by the public safety analytics gateway, the dataassociated with public safety from the data not associated with publicsafety; capturing, by the public safety analytics gateway, the dataassociated with public safety; transmitting, by the public safetyanalytics gateway, the data associated with public safety and the datanot associated with public safety to a gateway; storing, by the publicsafety analytics gateway, a plurality of records and generating outcomesfrom interaction with field based sensors; and facilitating, by theartificial intelligence engine, predictive alert conditions and responsescenarios.
 9. The apparatus of claim 8 wherein the operations furthercomprise: identifying search patterns from a first responder userdevice; applying metadata to the search patterns; refining the searchpatterns based on the identifying step, and generating searchrecommendations as a function of the metadata and an identification ofthe first responder user device.
 10. The apparatus of claim 8 whereinthe operations further comprise causing the data associated with publicsafety to be stored.
 11. The apparatus of claim 8 wherein the operationsfurther comprise analyzing the data associated with public safety inreal time and adjusting a data collection process based on whether thereis an emergency event.
 12. The apparatus of claim 8 wherein theoperations further comprise tracking a location of emergency vehicles ora location of first responders.
 13. The apparatus of claim 8 wherein theoperations further comprise receiving data from one of a plurality ofservers.
 14. The apparatus of claim 8 wherein the operations furthercomprise communicating with a server configured to aggregate dataassociated with public safety from a plurality of similarly configuredapparatuses.
 15. The apparatus of claim 8 wherein the operations furthercomprise integrating data from a plurality of public safety mobileapplications and a plurality of first responder user devices.
 16. Awireless network comprising: a network gateway in communication with aplurality of application servers; a public safety analytics gateway incommunication with the plurality of application servers, the publicsafety analytics gateway comprising a front end processor configured tocommunicate with the network gateway and a public safety enterpriseserver and a data collector in communication with the front endprocessor wherein the front end processor is configured to receivepublic safety data from the public service enterprise server and forwardthe public safety data to both the network gateway and the datacollector; a data analytics engine in communications with the datacollector and wherein the data analytics engine is configured to theanalyze public safety data; a recommendation engine in communicationwith the front end processor, the recommendation engine configured tocapture mobile search data associated with a first responder userdevice, store the mobile search data, and generate metadata associatedwith the mobile search data; and an artificial intelligence engine incommunication with the recommendation engine and the data analyticsengine wherein the artificial intelligence engine is configured to storea plurality of records and generate outcomes from interaction withfield-based sensors thereby facilitating predictive alert conditions andresponse scenarios.
 17. The wireless network of claim 16 furthercomprising a plurality of network gateways, a plurality of public safetyanalytics gateways wherein each public safety analytics gateway of theplurality of public safety analytics gateways is in communication withone or more network gateways of the plurality of network gateways, and amaster public safety analytics gateway in communication with each of theplurality of public safety analytics gateways, wherein the master publicsafety analytics gateway collects public safety data from the pluralityof public safety analytics gateways.